Handwriting Recognition System based on a deep Convolutional Recurrent Neural Network architecture

Related tags

Computer Vision machine-learning deep-learning tensorflow cnn rnn handwriting-recognition
Overview

Handwriting Recognition System

This repository is the Tensorflow implementation of the Handwriting Recognition System described in Handwriting Recognition of Historical Documents with Few Labeled Data (please cite the paper if you use this code in your research paper). This code was also used for the baseline system in Fine-tuning Handwriting Recognition systems with Temporal Dropout.

This code is free for academic and research use. For commercial use of the code please contact Edgard Chammas.

To help run the system, sample images from ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset are added.

Configuration

General configuration can be found in config.py

CNN-specific architecture configuration can be found in cnn.py

Training

python train.py

This will generate a text log file and a Tensorflow summary.

Decoding

python test.py

This will generate, for each image, the line transcription. The output will be written to decoded.txt by default.

python compute_probs.py

This will generate, for each image, the posterior probabilities at each timestep. Files will be stored in Probs by default.

Dependencies

  • Tensorflow
  • OpenCV-Python

Citation

Please cite the following paper if you use this code in your research paper:

@inproceedings{chammas2018handwriting,
  title={Handwriting Recognition of Historical Documents with few labeled data},
  author={Chammas, Edgard and Mokbel, Chafic and Likforman-Sulem, Laurence},
  booktitle={2018 13th IAPR International Workshop on Document Analysis Systems (DAS)},
  pages={43--48},
  year={2018},
  organization={IEEE}
}

Acknowledgment

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

Contributions

Feel free to send your pull request or open issues.

Comments
  • Problems trying to get this working with a Docker/Podman TF container . .

    Problems trying to get this working with a Docker/Podman TF container . .

    I am new to both Python (but familiar with C and Ruby) and TF - I have the TF container going OK with a "Hello World" but when I do this:

    # podman run -it --rm -v ~phr/src/python/HandwritingRecognitionSystem:/tmp -w /tmp tensorflow/tensorflow python train.py

    I get this:

    2020-05-04 19:57:54.104113: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory
2020-05-04 19:57:54.104167: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory
2020-05-04 19:57:54.104174: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
Traceback (most recent call last):
  File "train.py", line 11, in <module>
    import cv2
ImportError: No module named cv2

    It appears that the TF processor thinks I am trying to use an Nvidia GPU and I have tried a few options I found to tell the system there is no GPU but haven't had any luck yet . . - but there is also the missing module cv2 . .

    Suggestions?

    Thanks, Phil.

    opened by philiprhoades 3
  • Is this compatible with latest Tensorflow version?

    Is this compatible with latest Tensorflow version?

    I get the following error

    2020-01-23 10:04:15.612666: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory 2020-01-23 10:04:15.612724: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory 2020-01-23 10:04:15.612734: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. Traceback (most recent call last): File "config.py", line 12, in flags = tf.app.flags AttributeError: 'module' object has no attribute 'app'

    opened by stevethearkiv 3
  • Not working Python37

    Not working Python37

    c:\Python37>python.exe HR\train.py W0709 14:09:30.364569 6332 deprecation_wrapper.py:119] From HR\train.py:41: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

    W0709 14:09:30.366611 6332 deprecation_wrapper.py:119] From c:\Python37\HR\cnn. py:71: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variabl e_scope instead.

    W0709 14:09:30.367612 6332 deprecation_wrapper.py:119] From c:\Python37\HR\util .py:40: The name tf.truncated_normal is deprecated. Please use tf.random.truncat ed_normal instead.

    W0709 14:09:30.459672 6332 deprecation_wrapper.py:119] From c:\Python37\HR\util .py:47: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d inste ad.

    W0709 14:09:31.796136 6332 lazy_loader.py:50] The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see:

    • https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-su nset.md
    • https://github.com/tensorflow/addons
    • https://github.com/tensorflow/io (for I/O related ops) If you depend on functionality not listed there, please file an issue.

    W0709 14:09:31.797139 6332 deprecation.py:323] From c:\Python37\HR\rnn.py:73: L STMCell.init (from tensorflow.python.ops.rnn_cell_impl) is deprecated and wi ll be removed in a future version. Instructions for updating: This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by th at in Tensorflow 2.0. W0709 14:09:31.798139 6332 deprecation.py:323] From c:\Python37\HR\rnn.py:74: M ultiRNNCell.init (from tensorflow.python.ops.rnn_cell_impl) is deprecated an d will be removed in a future version. Instructions for updating: This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replace d by that in Tensorflow 2.0. W0709 14:09:31.800641 6332 deprecation.py:323] From c:\Python37\HR\rnn.py:81: b idirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version. Instructions for updating: Please use keras.layers.Bidirectional(keras.layers.RNN(cell)), which is equiva lent to this API W0709 14:09:31.801141 6332 deprecation.py:323] From c:\Python37\lib\site-packag es\tensorflow\python\ops\rnn.py:464: dynamic_rnn (from tensorflow.python.ops.rnn ) is deprecated and will be removed in a future version. Instructions for updating: Please use keras.layers.RNN(cell), which is equivalent to this API W0709 14:09:32.159433 6332 deprecation.py:506] From c:\Python37\lib\site-packag es\tensorflow\python\ops\rnn_cell_impl.py:961: calling Zeros.init (from tens orflow.python.ops.init_ops) with dtype is deprecated and will be removed in a fu ture version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the c onstructor W0709 14:09:32.971470 6332 deprecation.py:323] From c:\Python37\lib\site-packag es\tensorflow\python\ops\rnn.py:244: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where W0709 14:09:33.300187 6332 deprecation_wrapper.py:119] From HR\train.py:61: The name tf.nn.ctc_loss is deprecated. Please use tf.compat.v1.nn.ctc_loss instead.

    W0709 14:09:33.302189 6332 deprecation_wrapper.py:119] From HR\train.py:65: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar in stead.

    W0709 14:09:33.304190 6332 deprecation.py:323] From HR\train.py:70: to_int32 (f rom tensorflow.python.ops.math_ops) is deprecated and will be removed in a futur e version. Instructions for updating: Use tf.cast instead. W0709 14:09:33.309194 6332 deprecation.py:323] From HR\train.py:72: to_float (f rom tensorflow.python.ops.math_ops) is deprecated and will be removed in a futur e version. Instructions for updating: Use tf.cast instead. W0709 14:09:33.311257 6332 deprecation_wrapper.py:119] From HR\train.py:76: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_va riables instead.

    W0709 14:09:35.607799 6332 deprecation_wrapper.py:119] From HR\train.py:80: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOp timizer instead.

    Reading ./samples/Images//000033/READ17.000033_01_01_pg_line_1486644351571_780.p ng Reading ./samples/Images//000033/READ17.000033_01_02_pg_line_1486644356245_781.p ng Traceback (most recent call last): File "HR\train.py", line 95, in inputList, seqLens, targetList = ReadData(cfg.TRAIN_LOCATION, cfg.TRAIN_LIST , cfg.TRAIN_NB, WND_HEIGHT, WND_WIDTH, WND_SHIFT, VEC_PER_WND, cfg.TRAIN_TRANS) File "c:\Python37\HR\util.py", line 130, in ReadData h, w = np.shape(image) ValueError: not enough values to unpack (expected 2, got 0)

    c:\Python37>

    opened by JonhSilver 3
  • GENERATE CHECK POINT

    GENERATE CHECK POINT

    Hi, i passed the training step (using train.py file) and model directory is created but the thing is there is no model at all. However i can see the log file but no checkpoint at all.

    In shot, i want to ask how to generate a checkpoint (model) to test this script.

    Thanks

    opened by PrakharPrashantSingh 2
  • Bump tensorflow-gpu from 2.5.3 to 2.7.2

    Bump tensorflow-gpu from 2.5.3 to 2.7.2

    Bumps tensorflow-gpu from 2.5.3 to 2.7.2.

    Release notes

    Sourced from tensorflow-gpu's releases.

    TensorFlow 2.7.2

    Release 2.7.2

    This releases introduces several vulnerability fixes:

    TensorFlow 2.7.1

    Release 2.7.1

    This releases introduces several vulnerability fixes:

    • Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
    • Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
    • Fixes a heap OOB access in Dequantize (CVE-2022-21726)
    • Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
    • Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
    • Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
    • Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
    • Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
    • Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
    • Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
    • Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)

    ... (truncated)

    Changelog

    Sourced from tensorflow-gpu's changelog.

    Release 2.7.2

    This releases introduces several vulnerability fixes:

    Release 2.6.4

    This releases introduces several vulnerability fixes:

    • Fixes a code injection in saved_model_cli (CVE-2022-29216)
    • Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)
    • Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)
    • Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)
    • Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)
    • Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)
    • Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)
    • Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)
    • Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)
    • Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)
    • Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)
    • Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)
    • Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)
    • Fixes a segfault due to missing support for quantized types (CVE-2022-29205)
    • Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)

    ... (truncated)

    Commits
    • dd7b8a3 Merge pull request #56034 from tensorflow-jenkins/relnotes-2.7.2-15779
    • 1e7d6ea Update RELEASE.md
    • 5085135 Merge pull request #56069 from tensorflow/mm-cp-52488e5072f6fe44411d70c6af09e...
    • adafb45 Merge pull request #56060 from yongtang:curl-7.83.1
    • 01cb1b8 Merge pull request #56038 from tensorflow-jenkins/version-numbers-2.7.2-4733
    • 8c90c2f Update version numbers to 2.7.2
    • 43f3cdc Update RELEASE.md
    • 98b0a48 Insert release notes place-fill
    • dfa5cf3 Merge pull request #56028 from tensorflow/disable-tests-on-r2.7
    • 501a65c Disable timing out tests
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow-gpu from 2.5.3 to 2.6.4

    Bump tensorflow-gpu from 2.5.3 to 2.6.4

    Bumps tensorflow-gpu from 2.5.3 to 2.6.4.

    Release notes

    Sourced from tensorflow-gpu's releases.

    TensorFlow 2.6.4

    Release 2.6.4

    This releases introduces several vulnerability fixes:

    TensorFlow 2.6.3

    Release 2.6.3

    This releases introduces several vulnerability fixes:

    • Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)
    • Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)
    • Fixes a heap OOB access in Dequantize (CVE-2022-21726)
    • Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)
    • Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)
    • Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)
    • Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)
    • Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)
    • Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)
    • Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)
    • Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)
    • Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)

    ... (truncated)

    Changelog

    Sourced from tensorflow-gpu's changelog.

    Release 2.6.4

    This releases introduces several vulnerability fixes:

    Release 2.8.0

    Major Features and Improvements

    • tf.lite:

      • Added TFLite builtin op support for the following TF ops:
        • tf.raw_ops.Bucketize op on CPU.
        • tf.where op for data types tf.int32/tf.uint32/tf.int8/tf.uint8/tf.int64.
        • tf.random.normal op for output data type tf.float32 on CPU.
        • tf.random.uniform op for output data type tf.float32 on CPU.
        • tf.random.categorical op for output data type tf.int64 on CPU.

    • tensorflow.experimental.tensorrt:

      • conversion_params is now deprecated inside TrtGraphConverterV2 in favor of direct arguments: max_workspace_size_bytes, precision_mode, minimum_segment_size, maximum_cached_engines, use_calibration and

    ... (truncated)

    Commits
    • 33ed2b1 Merge pull request #56102 from tensorflow/mihaimaruseac-patch-1
    • e1ec480 Fix build due to importlib-metadata/setuptools
    • 63f211c Merge pull request #56033 from tensorflow-jenkins/relnotes-2.6.4-6677
    • 22b8fe4 Update RELEASE.md
    • ec30684 Merge pull request #56070 from tensorflow/mm-cp-adafb45c781-on-r2.6
    • 38774ed Merge pull request #56060 from yongtang:curl-7.83.1
    • 9ef1604 Merge pull request #56036 from tensorflow-jenkins/version-numbers-2.6.4-9925
    • a6526a3 Update version numbers to 2.6.4
    • cb1a481 Update RELEASE.md
    • 4da550f Insert release notes place-fill
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow-gpu from 2.4.1 to 2.5.2

    Bump tensorflow-gpu from 2.4.1 to 2.5.2

    Bumps tensorflow-gpu from 2.4.1 to 2.5.2.

    Release notes

    Sourced from tensorflow-gpu's releases.

    TensorFlow 2.5.2

    Release 2.5.2

    This release introduces several vulnerability fixes:

    • Fixes a code injection issue in saved_model_cli (CVE-2021-41228)
    • Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)
    • Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)
    • Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)
    • Fixes a heap OOB in SparseBinCount (CVE-2021-41226)
    • Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)
    • Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)
    • Fixes segfaults and vulnerabilities caused by accesses to invalid memory during shape inference in Cudnn* ops (CVE-2021-41221)
    • Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)
    • Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)
    • Fixes an undefined behavior via nullptr reference binding in sparse matrix multiplication (CVE-2021-41219)
    • Fixes a heap buffer overflow in Transpose (CVE-2021-41216)
    • Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)
    • Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)
    • Fixes an undefined behavior arising from reference binding to nullptr in tf.ragged.cross (CVE-2021-41214)
    • Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)
    • Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)
    • Fixes an FPE in ParallelConcat (CVE-2021-41207)
    • Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)
    • Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)
    • Fixes vulnerabilities caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes vulnerabilities caused by incomplete validation of shapes in multiple TF ops (CVE-2021-41206)
    • Fixes a segfault produced while copying constant resource tensor (CVE-2021-41204)
    • Fixes a vulnerability caused by unitialized access in EinsumHelper::ParseEquation (CVE-2021-41201)
    • Fixes several vulnerabilities and segfaults caused by missing validation during checkpoint loading (CVE-2021-41203)
    • Fixes an overflow producing a crash in tf.range (CVE-2021-41202)
    • Fixes an overflow producing a crash in tf.image.resize when size is large (CVE-2021-41199)
    • Fixes an overflow producing a crash in tf.tile when tiling tensor is large (CVE-2021-41198)
    • Fixes a vulnerability produced due to incomplete validation in tf.summary.create_file_writer (CVE-2021-41200)
    • Fixes multiple crashes due to overflow and CHECK-fail in ops with large tensor shapes (CVE-2021-41197)
    • Fixes a crash in max_pool3d when size argument is 0 or negative (CVE-2021-41196)
    • Fixes a crash in tf.math.segment_* operations (CVE-2021-41195)
    • Updates curl to 7.78.0 to handle CVE-2021-22922, CVE-2021-22923, CVE-2021-22924, CVE-2021-22925, and CVE-2021-22926.

    TensorFlow 2.5.1

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)

    ... (truncated)

    Changelog

    Sourced from tensorflow-gpu's changelog.

    Release 2.5.2

    This release introduces several vulnerability fixes:

    ... (truncated)

    Commits
    • 957590e Merge pull request #52873 from tensorflow-jenkins/relnotes-2.5.2-20787
    • 2e1d16d Update RELEASE.md
    • 2fa6dd9 Merge pull request #52877 from tensorflow-jenkins/version-numbers-2.5.2-192
    • 4807489 Merge pull request #52881 from tensorflow/fix-build-1-on-r2.5
    • d398bdf Disable failing test
    • 857ad5e Merge pull request #52878 from tensorflow/fix-build-1-on-r2.5
    • 6c2a215 Disable failing test
    • f5c57d4 Update version numbers to 2.5.2
    • e51f949 Insert release notes place-fill
    • 2620d2c Merge pull request #52863 from tensorflow/fix-build-3-on-r2.5
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow-gpu from 2.4.1 to 2.5.1

    Bump tensorflow-gpu from 2.4.1 to 2.5.1

    Bumps tensorflow-gpu from 2.4.1 to 2.5.1.

    Release notes

    Sourced from tensorflow-gpu's releases.

    TensorFlow 2.5.1

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)
    • Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)
    • Fixes a heap OOB in RaggedGather (CVE-2021-37641)
    • Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)
    • Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)
    • Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)
    • Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)
    • Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)
    • Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)
    • Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)
    • Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)
    • Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)
    • Fixes a use after free in boosted trees creation (CVE-2021-37652)
    • Fixes a division by 0 in ResourceGather (CVE-2021-37653)
    • Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)
    • Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse (CVE-2021-37656)
    • Fixes an undefined behavior arising from reference binding to nullptr in MatrixDiagV* ops (CVE-2021-37657)
    • Fixes an undefined behavior arising from reference binding to nullptr in MatrixSetDiagV* ops (CVE-2021-37658)
    • Fixes an undefined behavior arising from reference binding to nullptr and heap OOB in binary cwise ops (CVE-2021-37659)
    • Fixes a division by 0 in inplace operations (CVE-2021-37660)
    • Fixes a crash caused by integer conversion to unsigned (CVE-2021-37661)
    • Fixes an undefined behavior arising from reference binding to nullptr in boosted trees (CVE-2021-37662)
    • Fixes a heap OOB in boosted trees (CVE-2021-37664)
    • Fixes vulnerabilities arising from incomplete validation in QuantizeV2 (CVE-2021-37663)
    • Fixes vulnerabilities arising from incomplete validation in MKL requantization (CVE-2021-37665)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToVariant (CVE-2021-37666)
    • Fixes an undefined behavior arising from reference binding to nullptr in unicode encoding (CVE-2021-37667)
    • Fixes an FPE in tf.raw_ops.UnravelIndex (CVE-2021-37668)
    • Fixes a crash in NMS ops caused by integer conversion to unsigned (CVE-2021-37669)
    • Fixes a heap OOB in UpperBound and LowerBound (CVE-2021-37670)
    • Fixes an undefined behavior arising from reference binding to nullptr in map operations (CVE-2021-37671)
    • Fixes a heap OOB in SdcaOptimizerV2 (CVE-2021-37672)
    • Fixes a CHECK-fail in MapStage (CVE-2021-37673)
    • Fixes a vulnerability arising from incomplete validation in MaxPoolGrad (CVE-2021-37674)
    • Fixes an undefined behavior arising from reference binding to nullptr in shape inference (CVE-2021-37676)
    • Fixes a division by 0 in most convolution operators (CVE-2021-37675)
    • Fixes vulnerabilities arising from missing validation in shape inference for Dequantize (CVE-2021-37677)
    • Fixes an arbitrary code execution due to YAML deserialization (CVE-2021-37678)
    • Fixes a heap OOB in nested tf.map_fn with RaggedTensors (CVE-2021-37679)

    ... (truncated)

    Changelog

    Sourced from tensorflow-gpu's changelog.

    Release 2.5.1

    This release introduces several vulnerability fixes:

    • Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)
    • Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)
    • Fixes a null pointer dereference in CompressElement (CVE-2021-37637)
    • Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)
    • Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)
    • Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)
    • Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)
    • Fixes a heap OOB in RaggedGather (CVE-2021-37641)
    • Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)
    • Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)
    • Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)
    • Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)
    • Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)
    • Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)
    • Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)
    • Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)
    • Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)
    • Fixes a use after free in boosted trees creation (CVE-2021-37652)
    • Fixes a division by 0 in ResourceGather (CVE-2021-37653)
    • Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)
    • Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)
    • Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse

    ... (truncated)

    Commits
    • 8222c1c Merge pull request #51381 from tensorflow/mm-fix-r2.5-build
    • d584260 Disable broken/flaky test
    • f6c6ce3 Merge pull request #51367 from tensorflow-jenkins/version-numbers-2.5.1-17468
    • 3ca7812 Update version numbers to 2.5.1
    • 4fdf683 Merge pull request #51361 from tensorflow/mm-update-relnotes-on-r2.5
    • 05fc01a Put CVE numbers for fixes in parentheses
    • bee1dc4 Update release notes for the new patch release
    • 47beb4c Merge pull request #50597 from kruglov-dmitry/v2.5.0-sync-abseil-cmake-bazel
    • 6f39597 Merge pull request #49383 from ashahab/abin-load-segfault-r2.5
    • 0539b34 Merge pull request #48979 from liufengdb/r2.5-cherrypick
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • How to train the model on the full image recognition after the training on the samples

    How to train the model on the full image recognition after the training on the samples

    Hello, I found your code is amazing and give best results for the handwriting document recognition ,but when I work on it I found difficulties to train the model on the full page after the manually labeled text-line images in the samples?

    Please can you give me a samples for "how to train the model on full images and the .tru file of it"?

    because I am trying to run it on a different data set for my research and your paper is the state-of-the-art solution. and I study the config.py but I don't find the way to add full image after the train on lines image

    opened by engreememad 1
  • Training Data and `No checkpoint found`

    Training Data and `No checkpoint found`

    The README says

    To help run the system, sample images from ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset are added.

    I tried reproducing the results of your work by running python3 compute_probs.py and python3 test.py both of which yielded No checkpoint found after the script reads images successfully. Reading ./samples/Images//000033/READ17.000033_01_18_pg_line_1486644440417_797.png Reading ./samples/Images//000033/READ17.000033_01_19_pg_line_1486644445527_798.png Reading ./samples/Images//000033/READ17.000033_01_20_pg_line_1486644453911_799.png Initializing... 2021-04-06 08:41:30.589865: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorF low binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

    Would you mind sharing the checkpoint file?

    Can the ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset be used to train the model? If yes, please provide details on it's implementation. The link to the dataset in the README is also broken, is this the same dataset?

    Please Advise.

    Thank you for your time.

    opened by ArunLukeDsouza 1
  • Bump tensorflow-gpu from 1.13.1 to 2.4.0

    Bump tensorflow-gpu from 1.13.1 to 2.4.0

    Bumps tensorflow-gpu from 1.13.1 to 2.4.0.

    Release notes

    Sourced from tensorflow-gpu's releases.

    TensorFlow 2.4.0

    Release 2.4.0

    Major Features and Improvements

    • tf.distribute introduces experimental support for asynchronous training of models via the tf.distribute.experimental.ParameterServerStrategy API. Please see the tutorial to learn more.

    • MultiWorkerMirroredStrategy is now a stable API and is no longer considered experimental. Some of the major improvements involve handling peer failure and many bug fixes. Please check out the detailed tutorial on Multi-worker training with Keras.

    • Introduces experimental support for a new module named tf.experimental.numpy which is a NumPy-compatible API for writing TF programs. See the detailed guide to learn more. Additional details below.

    • Adds Support for TensorFloat-32 on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere based GPUs and is enabled by default.

    • A major refactoring of the internals of the Keras Functional API has been completed, that should improve the reliability, stability, and performance of constructing Functional models.

    • Keras mixed precision API tf.keras.mixed_precision is no longer experimental and allows the use of 16-bit floating point formats during training, improving performance by up to 3x on GPUs and 60% on TPUs. Please see below for additional details.

    • TensorFlow Profiler now supports profiling MultiWorkerMirroredStrategy and tracing multiple workers using the sampling mode API.

    • TFLite Profiler for Android is available. See the detailed guide to learn more.

    • TensorFlow pip packages are now built with CUDA11 and cuDNN 8.0.2.

    Breaking Changes

    • TF Core:

      • Certain float32 ops run in lower precsion on Ampere based GPUs, including matmuls and convolutions, due to the use of TensorFloat-32. Specifically, inputs to such ops are rounded from 23 bits of precision to 10 bits of precision. This is unlikely to cause issues in practice for deep learning models. In some cases, TensorFloat-32 is also used for complex64 ops. TensorFloat-32 can be disabled by running tf.config.experimental.enable_tensor_float_32_execution(False).
      • The byte layout for string tensors across the C-API has been updated to match TF Core/C++; i.e., a contiguous array of tensorflow::tstring/TF_TStrings.
      • C-API functions TF_StringDecode, TF_StringEncode, and TF_StringEncodedSize are no longer relevant and have been removed; see core/platform/ctstring.h for string access/modification in C.
      • tensorflow.python, tensorflow.core and tensorflow.compiler modules are now hidden. These modules are not part of TensorFlow public API.
      • tf.raw_ops.Max and tf.raw_ops.Min no longer accept inputs of type tf.complex64 or tf.complex128, because the behavior of these ops is not well defined for complex types.
      • XLA:CPU and XLA:GPU devices are no longer registered by default. Use TF_XLA_FLAGS=--tf_xla_enable_xla_devices if you really need them, but this flag will eventually be removed in subsequent releases.

    • tf.keras:

      • The steps_per_execution argument in model.compile() is no longer experimental; if you were passing experimental_steps_per_execution, rename it to steps_per_execution in your code. This argument controls the number of batches to run during each tf.function call when calling model.fit(). Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead.
      • A major refactoring of the internals of the Keras Functional API may affect code that is relying on certain internal details:
        • Code that uses isinstance(x, tf.Tensor) instead of tf.is_tensor when checking Keras symbolic inputs/outputs should switch to using tf.is_tensor.
        • Code that is overly dependent on the exact names attached to symbolic tensors (e.g. assumes there will be ":0" at the end of the inputs, treats names as unique identifiers instead of using tensor.ref(), etc.) may break.
        • Code that uses full path for get_concrete_function to trace Keras symbolic inputs directly should switch to building matching tf.TensorSpecs directly and tracing the TensorSpec objects.
        • Code that relies on the exact number and names of the op layers that TensorFlow operations were converted into may have changed.
        • Code that uses tf.map_fn/tf.cond/tf.while_loop/control flow as op layers and happens to work before TF 2.4. These will explicitly be unsupported now. Converting these ops to Functional API op layers was unreliable before TF 2.4, and prone to erroring incomprehensibly or being silently buggy.
        • Code that directly asserts on a Keras symbolic value in cases where ops like tf.rank used to return a static or symbolic value depending on if the input had a fully static shape or not. Now these ops always return symbolic values.
        • Code already susceptible to leaking tensors outside of graphs becomes slightly more likely to do so now.
        • Code that tries directly getting gradients with respect to symbolic Keras inputs/outputs. Use GradientTape on the actual Tensors passed to the already-constructed model instead.
        • Code that requires very tricky shape manipulation via converted op layers in order to work, where the Keras symbolic shape inference proves insufficient.
        • Code that tries manually walking a tf.keras.Model layer by layer and assumes layers only ever have one positional argument. This assumption doesn't hold true before TF 2.4 either, but is more likely to cause issues now.

    ... (truncated)

    Changelog

    Sourced from tensorflow-gpu's changelog.

    Release 2.4.0

    Major Features and Improvements

    Breaking Changes

    • TF Core:
      • Certain float32 ops run in lower precision on Ampere based GPUs, including

    ... (truncated)

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow-gpu from 2.5.3 to 2.9.3

    Bump tensorflow-gpu from 2.5.3 to 2.9.3

    Bumps tensorflow-gpu from 2.5.3 to 2.9.3.

    Release notes

    Sourced from tensorflow-gpu's releases.

    TensorFlow 2.9.3

    Release 2.9.3

    This release introduces several vulnerability fixes:

    TensorFlow 2.9.2

    Release 2.9.2

    This releases introduces several vulnerability fixes:

    ... (truncated)

    Changelog

    Sourced from tensorflow-gpu's changelog.

    Release 2.9.3

    This release introduces several vulnerability fixes:

    Release 2.8.4

    This release introduces several vulnerability fixes:

    ... (truncated)

    Commits
    • a5ed5f3 Merge pull request #58584 from tensorflow/vinila21-patch-2
    • 258f9a1 Update py_func.cc
    • cd27cfb Merge pull request #58580 from tensorflow-jenkins/version-numbers-2.9.3-24474
    • 3e75385 Update version numbers to 2.9.3
    • bc72c39 Merge pull request #58482 from tensorflow-jenkins/relnotes-2.9.3-25695
    • 3506c90 Update RELEASE.md
    • 8dcb48e Update RELEASE.md
    • 4f34ec8 Merge pull request #58576 from pak-laura/c2.99f03a9d3bafe902c1e6beb105b2f2417...
    • 6fc67e4 Replace CHECK with returning an InternalError on failing to create python tuple
    • 5dbe90a Merge pull request #58570 from tensorflow/r2.9-7b174a0f2e4
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
Owner
Edgard Chammas
Edgard Chammas
GitHub
Convolutional Recurrent Neural Networks(CRNN) for Scene Text Recognition

CRNN_Tensorflow This is a TensorFlow implementation of a Deep Neural Network for scene text recognition. It is mainly based on the paper "An End-to-En

MaybeShewill-CV 1000 Dec 27, 2022
Use Convolutional Recurrent Neural Network to recognize the Handwritten line text image without pre segmentation into words or characters. Use CTC loss Function to train.

Handwritten Line Text Recognition using Deep Learning with Tensorflow Description Use Convolutional Recurrent Neural Network to recognize the Handwrit

sushant097 224 Jan 7, 2023
ISI's Optical Character Recognition (OCR) software for machine-print and handwriting data

VistaOCR ISI's Optical Character Recognition (OCR) software for machine-print and handwriting data Publications "How to Efficiently Increase Resolutio

ISI Center for Vision, Image, Speech, and Text Analytics 21 Dec 8, 2021
This is the implementation of the paper "Gated Recurrent Convolution Neural Network for OCR"

Gated Recurrent Convolution Neural Network for OCR This project is an implementation of the GRCNN for OCR. For details, please refer to the paper: htt

null 90 Dec 22, 2022
IMGUR5K handwriting set. It is a handwritten in-the-wild dataset, which contains challenging real world handwritten samples from different writers.The dataset is shared as a set of image urls with annotations. This code downloads the images and verifies the hash to the image to avoid data contamination.

IMGUR5K Handwriting Dataset To run the code for downloading the urls and generate corresponding annotations : Usage: python download_imgur5k.py --data

Facebook Research 213 Dec 26, 2022
Python package for handwriting and sketching in Jupyter cells

ipysketch A Python package for handwriting and sketching in Jupyter notebooks. Usage A movie is worth a thousand pictures is worth a million words...

Matthias Baer 16 Jan 5, 2023
Convert Text-to Handwriting Using Python

Convert Text-to Handwriting Using Python Description In this project we'll use python library that's "pywhatkit" for converting text to handwriting. t

null 8 Nov 19, 2022
This tool will help you convert your text to handwriting xD

So your teacher asked you to upload written assignments? Hate writing assigments? This tool will help you convert your text to handwriting xD

Saurabh Daware 4.2k Jan 7, 2023
Handwritten Text Recognition (HTR) system implemented with TensorFlow (TF) and trained on the IAM off-line HTR dataset. This Neural Network (NN) model recognizes the text contained in the images of segmented words.

Handwritten-Text-Recognition Handwritten Text Recognition (HTR) system implemented with TensorFlow (TF) and trained on the IAM off-line HTR dataset. T

null 27 Jan 8, 2023
This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

TransFG: A Transformer Architecture for Fine-grained Recognition Official PyTorch code for the paper: TransFG: A Transformer Architecture for Fine-gra

Ju He 307 Jan 3, 2023
A curated list of resources for text detection/recognition (optical character recognition ) with deep learning methods.

awesome-deep-text-detection-recognition A curated list of awesome deep learning based papers on text detection and recognition. Text Detection Papers

null 2.4k Jan 8, 2023
Text recognition (optical character recognition) with deep learning methods.

What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis | paper | training and evaluation data | failure cases and cle

Clova AI Research 3.2k Jan 4, 2023
Sign Language Recognition service utilizing a deep learning model with Long Short-Term Memory to perform sign language recognition.

Sign Language Recognition Service This is a Sign Language Recognition service utilizing a deep learning model with Long Short-Term Memory to perform s

Martin Lønne 1 Jan 8, 2022
Code for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition

STN-OCR: A single Neural Network for Text Detection and Text Recognition This repository contains the code for the paper: STN-OCR: A single Neural Net

Christian Bartz 496 Jan 5, 2023
TextBoxes: A Fast Text Detector with a Single Deep Neural Network https://github.com/MhLiao/TextBoxes 基于SSD改进的文本检测算法，textBoxes_note记录了之前整理的笔记。

TextBoxes: A Fast Text Detector with a Single Deep Neural Network Introduction This paper presents an end-to-end trainable fast scene text detector, n

zhangjing1 24 Apr 28, 2022
Handwritten Text Recognition (HTR) system implemented with TensorFlow.

Handwritten Text Recognition with TensorFlow Update 2021: more robust model, faster dataloader, word beam search decoder also available for Windows Up

Harald Scheidl 1.5k Jan 7, 2023
Polaris is a Face recognition attendance system .

Support Me ?? About Polaris ?? Polaris is a system based on facial recognition with a futuristic GUI design, Can easily find people informations store

XN3UR0N 215 Dec 26, 2022
MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition

MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition Python 2.7 Python 3.6 MORAN is a network with rectification mechanism for

Canjie Luo 595 Dec 27, 2022
EQFace: An implementation of EQFace: A Simple Explicit Quality Network for Face Recognition

EQFace: A Simple Explicit Quality Network for Face Recognition The first face recognition network that generates explicit face quality online.

DeepCam Shenzhen 141 Dec 31, 2022