Implementation of EAST scene text detector in Keras

Jan Zdenek

Last update: Nov 15, 2022

Overview

EAST: An Efficient and Accurate Scene Text Detector

This is a Keras implementation of EAST based on a Tensorflow implementation made by argman.

The original paper by Zhou et al. is available on arxiv.

Only RBOX geometry is implemented
Differences from the original paper
- Uses ResNet-50 instead of PVANet
- Uses dice loss function instead of balanced binary cross-entropy
- Uses AdamW optimizer instead of the original Adam

The implementation of AdamW optimizer is borrowed from this repository.

The code should run under both Python 2 and Python 3.

Requirements

Keras 2.0 or higher, and TensorFlow 1.0 or higher should be enough.

The code should run with Keras 2.1.5. If you use Keras 2.2 or higher, you have to remove ZeroPadding2D from the model.py file. Specifically, replace the line containing ZeroPadding2D with x = concatenate([x, resnet.get_layer('activation_10').output], axis=3).

I will add a list of packages and their versions under which no errors should occur later.

Data

You can use your own data, but the annotation files need to conform the ICDAR 2015 format.

ICDAR 2015 dataset can be downloaded from this site. You need the data from Task 4.1 Text Localization.
You can also download the MLT dataset, which uses the same annotation style as ICDAR 2015, there.

Alternatively, you can download a training dataset consisting of all training images from ICDAR 2015 and ICDAR 2013 datasets with annotation files in ICDAR 2015 format here.
You can also get a subset of validation images from the MLT 2017 dataset containing only images with text in the Latin alphabet for validation here.
The original datasets are distributed by the organizers of the Robust Reading Competition and are licensed under the CC BY 4.0 license.

Training

You need to put all of your training images and their corresponding annotation files in one directory. The annotation files have to be named gt_IMAGENAME.txt.
You also need a directory for validation data, which requires the same structure as the directory with training images.

Training is started by running train.py. It accepts several arguments including path to training and validation data, and path where you want to save trained checkpoint models. You can see all of the arguments you can specify in the train.py file.

Execution example

python train.py --gpu_list=0,1 --input_size=512 --batch_size=12 --nb_workers=6 --training_data_path=../data/ICDAR2015/train_data/ --validation_data_path=../data/MLT/val_data_latin/ --checkpoint_path=tmp/icdar2015_east_resnet50/

You can download a model trained on ICDAR 2015 and 2013 here. It achieves 0.802 F-score on ICDAR 2015 test set. You also need to download this JSON file of the model to be able to use it.

Test

The images you want to classify have to be in one directory, whose path you have to pass as an argument. Classification is started by running eval.py with arguments specifying path to the images to be classified, the trained model, and a directory which you want to save the output in.

Execution example

python eval.py --gpu_list=0 --test_data_path=../data/ICDAR2015/test/ --model_path=tmp/icdar2015_east_resnet50/model_XXX.h5 --output_dir=tmp/icdar2015_east_resnet50/eval/

Detection examples

Comments

IndexError: index 1 is out of bounds for axis 0 with size 1

Traceback (most recent call last): File "train.py", line 249, in main() File "train.py", line 246, in main history = parallel_model.fit_generator(train_data_generator, epochs=FLAGS.max_epochs, steps_per_epoch=train_samples_count/FLAGS.batch_size, use_multiprocessing=False, callbacks=callbacks, verbose=1) File "C:\Users\VINOTH KUMAR S\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper return func(*args, **kwargs) File "C:\Users\VINOTH KUMAR S\Anaconda3\lib\site-packages\keras\engine\training.py", line 1418, in fit_generator initial_epoch=initial_epoch) File "C:\Users\VINOTH KUMAR S\Anaconda3\lib\site-packages\keras\engine\training_generator.py", line 251, in fit_generator callbacks.on_epoch_end(epoch, epoch_logs) File "C:\Users\VINOTH KUMAR S\Anaconda3\lib\site-packages\keras\callbacks.py", line 79, in on_epoch_end callback.on_epoch_end(epoch, logs) File "train.py", line 104, in on_epoch_end input_image_summary = make_image_summary(((data[0][0][i] + 1) * 127.5).astype('uint8')) IndexError: index 1 is out of bounds for axis 0 with size 1

opened by vinothkumarsvv 23
How should i annotate my images for training my own model

Hi Kurapan, Thanks very much for the implementation in Keras. Iam planning to train the whole model for my custom dataset. How should i annotate my images. I mean i have already annotated my images with x1,x2,y1,y2 but i dont have any detected word on it. Also i used just rectangular bounding box. Should i use polygons for it? How should i do it ? Is there any specific tool in which i can get annotations in gttext file format .

for example: 886,144,934,141,932,157,884,160,smrt 869,67,920,61,923,85,872,91,citi

opened by kalai2033 12

Error: timeout value too large in multiprocessor

Hi, I am getting this error. Did you every get any such error. If so, can you please suggest how to resolve it?

  File "D:/Documents/PythonScripts/Keras_EAST/train.py", line 213, in main
    val_data = data_processor.load_data(FLAGS)

  File "D:\Documents\PythonScripts\Keras_EAST\data_processor.py", line 864, in load_data
    loaded_data = pool.map_async(load_data_process, zip(image_files, itertools.repeat(FLAGS), itertools.repeat(is_train))).get(9999999)

  File "c:\winpython\python-3.5.4.amd64\lib\multiprocessing\pool.py", line 638, in get
    self.wait(timeout)

  File "c:\winpython\python-3.5.4.amd64\lib\multiprocessing\pool.py", line 635, in wait
    self._event.wait(timeout)

  File "c:\winpython\python-3.5.4.amd64\lib\threading.py", line 549, in wait
    signaled = self._cond.wait(timeout)

wontfix

opened by wajahat57 10

OSError: Unable to open file h5py.h5f

I have been able to train the model, but when trying to test the model, I got this error message: File "/home/paperspace/.local/lib/python3.6/site-packages/h5py/_hl/files.py", line 170, in make_fid fid = h5f.open(name, flags, fapl=fapl) File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper File "h5py/h5f.pyx", line 85, in h5py.h5f.open OSError: Unable to open file (file read failed: time = Thu Feb 21 01:07:23 2019

Any suggestion?

opened by niddal-imam 5

tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match:

Hello. I am trying to implement this in Tensorflow 2. So I have converted all the codes in tf2.1.0. But during training in the first epoch itself I get this error: File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 6653, in raise_from_not_ok_status six.raise_from(core._status_to_exception(e.code, message), None) File "", line 3, in raise_from tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [16,128,128,128] vs. shape[1] = [16,64,64,128] [Op:ConcatV2] name: concat

I am not able to figure out what is the issue. It might be with the EAST model I am implementing. Since ResNet has now been implemented from tensorflow.keras so I have changed the concat layers.

Code for models.py: #import keras from tensorflow.keras.applications.resnet50 import ResNet50 from tensorflow.keras.models import Model from tensorflow.keras.layers import Conv2D, concatenate, BatchNormalization, Lambda, Input, multiply, add, ZeroPadding2D, Activation, Layer, MaxPooling2D, Dropout from tensorflow.keras import regularizers #import keras.backend as K import tensorflow as tf import numpy as np

RESIZE_FACTOR = 2

def resize_bilinear(x): return tf.image.resize(x, size=(tf.shape(x)[1]*RESIZE_FACTOR, tf.shape(x)[2]*RESIZE_FACTOR))

class EAST_model:

def __init__(self, input_size=512):
    input_image = Input(shape=(None, None, 3), name='input_image')
    overly_small_text_region_training_mask = Input(shape=(None, None, 1), name='overly_small_text_region_training_mask')
    text_region_boundary_training_mask = Input(shape=(None, None, 1), name='text_region_boundary_training_mask')
    target_score_map = Input(shape=(None, None, 1), name='target_score_map')
    resnet = ResNet50(input_tensor=input_image, weights='imagenet', include_top=False, pooling=None)
    x = resnet.get_layer('conv3_block4_2_relu').output
    x = Lambda(resize_bilinear, name='resize_1')(x)
    x = concatenate([x, resnet.get_layer('conv3_block1_2_relu').output], axis=3)
    x = Conv2D(128, (1, 1), padding='same', kernel_regularizer=regularizers.l2(1e-5))(x)
    x = BatchNormalization(momentum=0.997, epsilon=1e-5, scale=True)(x)
    x = Activation('relu')(x)
    x = Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(1e-5))(x)
    x = BatchNormalization(momentum=0.997, epsilon=1e-5, scale=True)(x)
    x = Activation('relu')(x)

    x = Lambda(resize_bilinear, name='resize_2')(x)
    x = concatenate([x, resnet.get_layer('conv3_block2_1_relu').output], axis=3)
    x = Conv2D(64, (1, 1), padding='same', kernel_regularizer=regularizers.l2(1e-5))(x)
    x = BatchNormalization(momentum=0.997, epsilon=1e-5, scale=True)(x)
    x = Activation('relu')(x)
    x = Conv2D(64, (3, 3), padding='same', kernel_regularizer=regularizers.l2(1e-5))(x)
    x = BatchNormalization(momentum=0.997, epsilon=1e-5, scale=True)(x)
    x = Activation('relu')(x)

    x = Lambda(resize_bilinear, name='resize_3')(x)
    x = concatenate([x, resnet.get_layer('conv1_relu').output], axis=3)
    x = Conv2D(32, (1, 1), padding='same', kernel_regularizer=regularizers.l2(1e-5))(x)
    x = BatchNormalization(momentum=0.997, epsilon=1e-5, scale=True)(x)
    x = Activation('relu')(x)
    x = Conv2D(32, (3, 3), padding='same', kernel_regularizer=regularizers.l2(1e-5))(x)
    x = BatchNormalization(momentum=0.997, epsilon=1e-5, scale=True)(x)
    x = Activation('relu')(x)

    x = Conv2D(32, (3, 3), padding='same', kernel_regularizer=regularizers.l2(1e-5))(x)
    x = BatchNormalization(momentum=0.997, epsilon=1e-5, scale=True)(x)
    x = Activation('relu')(x)

    pred_score_map = Conv2D(1, (1, 1), activation=tf.nn.sigmoid, name='pred_score_map')(x)
    rbox_geo_map = Conv2D(4, (1, 1), activation=tf.nn.sigmoid, name='rbox_geo_map')(x) 
    rbox_geo_map = Lambda(lambda x: x * input_size)(rbox_geo_map)
    angle_map = Conv2D(1, (1, 1), activation=tf.nn.sigmoid, name='rbox_angle_map')(x)
    angle_map = Lambda(lambda x: (x - 0.5) * np.pi / 2)(angle_map)
    pred_geo_map = concatenate([rbox_geo_map, angle_map], axis=3, name='pred_geo_map')

    model = Model(inputs=[input_image, overly_small_text_region_training_mask, text_region_boundary_training_mask, target_score_map], outputs=[pred_score_map, pred_geo_map])
    
    self.model = model
    self.input_image = input_image
    self.overly_small_text_region_training_mask = overly_small_text_region_training_mask
    self.text_region_boundary_training_mask = text_region_boundary_training_mask
    self.target_score_map = target_score_map
    self.pred_score_map = pred_score_map
    self.pred_geo_map = pred_geo_map

Please let me know. I have been trying to solve this issue for a while now.

opened by nehasaraf1994 4

overly_small_text_region_training_mask

If some the text in an image is to small, it is excluded from the training by this piece of code: if min(poly_h, poly_w) < FLAGS.min_text_size: cv2.fillPoly(overly_small_text_region_training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0)

However, the same text is excluded also if "tag" is true. WHY? if tag: cv2.fillPoly(overly_small_text_region_training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0)

opened by jhaggle 4
Locality Aware NMS as separate package

The bit where you compile the NMS package is ... brilliant ... if a bit scary. It might be a bit cleaner if that was its own separate package on PyPI and this package were purely python.

I can help get that done and build self contained wheels for it as well (at least for Linux). Interested?

opened by safijari 4
Terminology

Could you explain the following terms:

geo_maps score_maps image_fns geo_map_channels overly_small_text_region_training_mask text_region_boundary_training_mask

opened by jhaggle 3
how can i use the json model

is there any code in your github to use the json model? I have trained the model and the model parameters have been saved in the json file.How can i use?

opened by fanfusuzi 3
Getting Error while started training

UserWarning('Using a generator with use_multiprocessing=True' Epoch 1/800 Exception in thread Thread-6: Traceback (most recent call last): File "C:\Users\Aquib\Anaconda3\lib\threading.py", line 916, in _bootstrap_inner self.run() File "C:\Users\Aquib\Anaconda3\lib\threading.py", line 864, in run self._target(*self._args, **self._kwargs) File "C:\Users\Aquib\Anaconda3\lib\site-packages\keras\utils\data_utils.py", line 666, in _run with closing(self.executor_fn(_SHARED_SEQUENCES)) as executor: File "C:\Users\Aquib\Anaconda3\lib\site-packages\keras\utils\data_utils.py", line 661, in initargs=(seqs, self.random_seed)) File "C:\Users\Aquib\Anaconda3\lib\multiprocessing\context.py", line 119, in Pool context=self.get_context()) File "C:\Users\Aquib\Anaconda3\lib\multiprocessing\pool.py", line 174, in init self._repopulate_pool() File "C:\Users\Aquib\Anaconda3\lib\multiprocessing\pool.py", line 239, in _repopulate_pool w.start() File "C:\Users\Aquib\Anaconda3\lib\multiprocessing\process.py", line 105, in start self._popen = self._Popen(self) File "C:\Users\Aquib\Anaconda3\lib\multiprocessing\context.py", line 322, in _Popen return Popen(process_obj) File "C:\Users\Aquib\Anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 65, in init reduction.dump(process_obj, to_child) File "C:\Users\Aquib\Anaconda3\lib\multiprocessing\reduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj) TypeError: can't pickle generator objects

opened by smaliaquib 3
Error loading pretrained model.json

Hi. Thank you very much for your EAST implementation in Keras. When I download the pretrained model from the link in README and run eval.py, it raised an error. File "eval.py", line 194, in main() File "eval.py", line 140, in main model = model_from_json(loaded_model_json, custom_objects={'tf': tf, 'RESIZE_FACTOR': RESIZE_FACTOR}) File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 492, in model_from_json return deserialize(config, custom_objects=custom_objects) File "/usr/local/lib/python3.5/dist-packages/keras/layers/init.py", line 55, in deserialize printable_module_name='layer') File "/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py", line 145, in deserialize_keras_object list(custom_objects.items()))) File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1032, in from_config process_node(layer, node_data) File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 991, in process_node layer(unpack_singleton(input_tensors), **kwargs) File "/usr/local/lib/python3.5/dist-packages/keras/engine/base_layer.py", line 457, in call output = self.call(inputs, **kwargs) File "/usr/local/lib/python3.5/dist-packages/keras/layers/core.py", line 687, in call return self.function(inputs, **arguments) File "/home/u00012/code/Text-Detection/EAST-keras/model.py", line 13, in resize_bilinear return tf.image.resize_bilinear(x, size=[K.shape(x)[1]*RESIZE_FACTOR, K.shape(x)[2]*RESIZE_FACTOR])

opened by unik00 3
Bump pillow from 8.1.1 to 9.3.0
Bumps pillow from 8.1.1 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]

909dc64 9.3.0 version bump

1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer

2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec

744f455 Added release notes

0846bfa Add to release notes

799a6a0 Fix linting

00b25fd Hide UserWarning in logs

05b175e Tighter test case

13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD

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dependencies
opened by dependabot[bot] 0
can we custom train for specific number detection like credit card number

Can we custom train for specific text because i don't want to use object detection. Can we train east text detector detect only particular sequence of number like credit card number type sequence from entire document.

opened by mohit-217 0
Bump numpy from 1.12.1 to 1.22.0
Bumps numpy from 1.12.1 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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dependencies
opened by dependabot[bot] 0
Bump ipython from 6.1.0 to 7.16.3
Bumps ipython from 6.1.0 to 7.16.3.

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d43c7c7 release 7.16.3

5fa1e40 Merge pull request from GHSA-pq7m-3gw7-gq5x

8df8971 back to dev

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bcae8e0 Backport PR #13335: What's new 7.16.2

8fcdcd3 Pin Jedi to <0.17.2.

2486838 release 7.16.1

20bdc6f fix conda build

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dependencies
opened by dependabot[bot] 0
Unable to run eval.py via the provided json pre-trained model.

Faced many challenges related to the tf and keras compatible version. After a lot of hassles, I was able to resolve the dependencies of libraries needed. Now, I am stuck at the below issue where it is asking for some marshal code. I believe this is coming from the custom objects passed into the model_from_json api call in eval.py line 140. Please advise or let me know if there is any updated stable version of the codebase.

Using TensorFlow backend. find: -xtype: unknown primary or operator make: `adaptor.so' is up to date. WARNING:tensorflow:From /Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:349: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From /Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3147: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

WARNING:tensorflow:From /Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:99: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

WARNING:tensorflow:From /Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3014: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

Traceback (most recent call last): File "eval.py", line 194, in main() File "eval.py", line 140, in main model = model_from_json(loaded_model_json)#, custom_objects={'tf': tf})#, 'RESIZE_FACTOR': RESIZE_FACTOR}) File "/Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/models.py", line 325, in model_from_json return layer_module.deserialize(config, custom_objects=custom_objects) File "/Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/layers/init.py", line 46, in deserialize printable_module_name='layer') File "/Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/utils/generic_utils.py", line 140, in deserialize_keras_object list(custom_objects.items()))) File "/Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/engine/topology.py", line 2361, in from_config process_layer(layer_data) File "/Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/engine/topology.py", line 2330, in process_layer custom_objects=custom_objects) File "/Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/layers/init.py", line 46, in deserialize printable_module_name='layer') File "/Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/utils/generic_utils.py", line 140, in deserialize_keras_object list(custom_objects.items()))) File "/Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/layers/core.py", line 706, in from_config function = func_load(config['function'], globs=globs) File "/Users/ganjha/opt/anaconda3/envs/eastv1/lib/python3.7/site-packages/keras/utils/generic_utils.py", line 200, in func_load code = marshal.loads(code.encode('raw_unicode_escape')) ValueError: bad marshal data (unknown type code)

opened by ganeshjha 0

Owner

Jan Zdenek

GitHub

This is a pytorch re-implementation of EAST: An Efficient and Accurate Scene Text Detector.

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A tensorflow implementation of EAST text detector

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AdvancedEAST is an algorithm used for Scene image text detect, which is primarily based on EAST, and the significant improvement was also made, which make long text predictions more accurate.https://github.com/huoyijie/raspberrypi-car

AdvancedEAST AdvancedEAST is an algorithm used for Scene image text detect, which is primarily based on EAST:An Efficient and Accurate Scene Text Dete

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pytextractor python ocr using tesseract/ with EAST opencv text detector Uses the EAST opencv detector defined here with pytesseract to extract text(de

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OCR, Scene-Text-Understanding, Text Recognition

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354 Dec 12, 2022

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215 Dec 7, 2022

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188 Dec 28, 2022

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

24 Apr 28, 2022

Implementation of EAST scene text detector in Keras

Related tags

Overview

EAST: An Efficient and Accurate Scene Text Detector

Requirements

Data

Training

Execution example

Test

Execution example

Detection examples

Comments

9.3.0

Changes

9.3.0 (2022-10-29)

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Owner

Jan Zdenek

This is a pytorch re-implementation of EAST: An Efficient and Accurate Scene Text Detector.

PyTorch Re-Implementation of EAST: An Efficient and Accurate Scene Text Detector

A tensorflow implementation of EAST text detector

AdvancedEAST is an algorithm used for Scene image text detect, which is primarily based on EAST, and the significant improvement was also made, which make long text predictions more accurate.https://github.com/huoyijie/raspberrypi-car

python ocr using tesseract/ with EAST opencv detector

EAST for ICPR MTWI 2018 Challenge II (Text detection of network images)

TextBoxes++: A Single-Shot Oriented Scene Text Detector

Forked from argman/EAST for the ICPR MTWI 2018 CHALLENGE

An Implementation of the alogrithm in paper IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection

OCR, Scene-Text-Understanding, Text Recognition

Implement 'Single Shot Text Detector with Regional Attention, ICCV 2017 Spotlight'

Single Shot Text Detector with Regional Attention

Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector

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。

caffe re-implementation of R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection

Implementation of our paper 'PixelLink: Detecting Scene Text via Instance Segmentation' in AAAI2018

MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition

Scene text recognition

Repository for Scene Text Detection with Supervised Pyramid Context Network with tensorflow.

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio