Kernel dying while downloading processing IAM dataset

Kernel Restarting The kernel for projects/DocByte/handwriting_recog/amazon_HWR/handwritten-text-recognition-for-apache-mxnet/0_handwriting_ocr.ipynb appears to have died. It will restart automatically.

test_ds = IAMDataset("form_original", train=False) running this line

getting the below error while executing dataset creation step. train_ds = IAMDataset("form", output_data="bb", output_parse_method="form", train=True)

Below code in 'Denoising text ouptut' section of '0_handwriting_ocr.ipynb' file, when run on a machine with 35.35GB RAM and 107.77GB disk space(google colab TPU Session) crashes system for unknown reason.

ctx_nlp = mx.gpu(3)
language_model, vocab = nlp.model.big_rnn_lm_2048_512(dataset_name='gbw', pretrained=True, ctx=ctx_nlp)
moses_tokenizer = nlp.data.SacreMosesTokenizer()
moses_detokenizer = nlp.data.SacreMosesDetokenizer()

How do i download this dataset without crashing the machine? And also, I don't want to download it from next time, so can I save this dataset too?

Hi again,

I am a bit confused about this error, happening in the 4_text_denoising notebook. I just did every step from before but something does not fit with the dimensions. Can you explain why this is happening?

AssertionErrorTraceback (most recent call last)
<ipython-input-31-2a0e848a57c4> in <module>
      1 model_path = 'models/denoiser2.params'
      2 if (os.path.isfile(model_path)):
----> 3     net.load_parameters(model_path, ctx=ctx)
      4     print("Loaded parameters")
      5     best_test_loss = evaluate(net, val_data_ft)

/usr/local/lib/python3.6/dist-packages/mxnet/gluon/block.py in load_parameters(self, filename, ctx, allow_missing, ignore_extra, cast_dtype, dtype_source)
    553                         name, filename, _brief_print_list(self._params.keys())))
    554             if name in params:
--> 555                 params[name]._load_init(loaded[name], ctx, cast_dtype=cast_dtype, dtype_source=dtype_source)
    556 
    557     def load_params(self, filename, ctx=None, allow_missing=False,

/usr/local/lib/python3.6/dist-packages/mxnet/gluon/parameter.py in _load_init(self, data, ctx, cast_dtype, dtype_source)
    280                     "Failed loading Parameter '%s' from saved params: " \
    281                     "shape incompatible expected %s vs saved %s"%(
--> 282                         self.name, str(self.shape), str(data.shape))
    283             self.shape = tuple(i if i != unknown_dim_size else j
    284                                for i, j in zip(self.shape, data.shape))

AssertionError: Failed loading Parameter 'transformer_enc_const' from saved params: shape incompatible expected (150, 512) vs saved (150, 256)

The 3_handwriting_recognition.py works fine with IAMDataset("line", output_data="text", train=True) but crashes when using the word IAMDataset. Specifically, doing this crashes the code.

train_ds = IAMDataset("word", output_data="text", train=True)
print("Number of training samples: {}".format(len(train_ds)))

test_ds = IAMDataset("word", output_data="text", train=False)
print("Number of testing samples: {}".format(len(test_ds)))

Gives: mxnet.base.MXNetError: Shape inconsistent, Provided = [13320192], inferred shape=(8863744,)

Hi,

Thanks a lot for this wonderful piece of work.

I am trying to calculate CTC loss to compute labeling probability after prediction. Please guide if it is possible

Thanks

Hi @jonomon The number of training and testing samples comes out to be zero on running the following code in 2_line_word_segmentation.ipynb -

train_ds = IAMDataset("form_bb", output_data="bb", output_parse_method=detection_box, train=True)
print("Number of training samples: {}".format(len(train_ds)))

Im working on colab, have included the extracted as well as the .tgz files in dataset/iamdataset. I can't seem to figure out the issue here, could you point me in the right direction?

Thanks, Sambbhav

Good work, thanks. I am using pre-trained models to get text from images. When I was going through the codes on how to do it, I learned that my test images's format has to match the format 'IAMDastaset' class in 'ocr.utils.iam_dataset' outputs. So, How do I modify 'IAMDastaset' class in 'ocr.utils.iam_dataset' to change an input image to match test dataframe format that this class outputs or How do I get dataframe for images other than the ones in IAM dataset. I couldn't understand this class completely. So, if anyone worked on this, please help me solve this.

When executing the code in the handwriting notebook I get an error in the segmentation, which I show below: LocalFileSystem :: Open "models / paragraph_segmentation2.params": No such file or directory

however, when I check the address I have the file resnet34_v1-48216ba9.params but in the same way I get the same error

could someone help me understand this mistake?

Here is my input image in color: 1. The detection that happended from pre-trained models can be seen below, when the above image is converted from 0. RGB to grayscale and 1. BGR to grayscale (happended at paragraph segmentation from '0_handwriting_ocr.ipynb' notebook & I have made no changes in the code from that notebook): 2. Because of this improper region detection, areas that actually has text are being cropped out. And, when i try form size other than what is in the code(because my images have smaller aspect ratio), that is form_size = (1120, 800), computer crashes. What is causing this and how can i not have this happen? 3. Presumably, because of the above improper detection or may be becuase line/word segmentaion not happening properly, here's the word segmentation: and here's the line segmentation: How do i fix these?

Hi, I already mentioned my problem but I didin't find an issue describing what I experience at the moment. When I start the 2_line_word_segmentation.ipynb I get the following error:

ConnectionResetErrorTraceback (most recent call last)
<ipython-input-13-fbd64d2ad138> in <module>
      3     cls_metric = mx.metric.Accuracy()
      4     box_metric = mx.metric.MAE()
----> 5     train_loss = run_epoch(e, net, train_data, trainer, log_dir, print_name="train", is_train=True, update_metric=False)
      6     test_loss = run_epoch(e, net, test_data, trainer, log_dir, print_name="test", is_train=False, update_metric=True)
      7     if test_loss < best_test_loss:

<ipython-input-6-6b90c6f2ae19> in run_epoch(e, network, dataloader, trainer, log_dir, print_name, is_train, update_metric)
     32 
     33     total_losses = [0 for ctx_i in ctx]
---> 34     for i, (X, Y) in enumerate(dataloader):
     35         X = gluon.utils.split_and_load(X, ctx)
     36         Y = gluon.utils.split_and_load(Y, ctx)

/usr/local/lib/python3.6/dist-packages/mxnet/gluon/data/dataloader.py in __next__(self)
    503         try:
    504             if self._dataset is None:
--> 505                 batch = pickle.loads(ret.get(self._timeout))
    506             else:
    507                 batch = ret.get(self._timeout)

/usr/local/lib/python3.6/dist-packages/mxnet/gluon/data/dataloader.py in rebuild_ndarray(pid, fd, shape, dtype)
     59             fd = multiprocessing.reduction.rebuild_handle(fd)
     60         else:
---> 61             fd = fd.detach()
     62         return nd.NDArray(nd.ndarray._new_from_shared_mem(pid, fd, shape, dtype))
     63 

/usr/lib/python3.6/multiprocessing/resource_sharer.py in detach(self)
     55         def detach(self):
     56             '''Get the fd.  This should only be called once.'''
---> 57             with _resource_sharer.get_connection(self._id) as conn:
     58                 return reduction.recv_handle(conn)
     59 

/usr/lib/python3.6/multiprocessing/resource_sharer.py in get_connection(ident)
     85         from .connection import Client
     86         address, key = ident
---> 87         c = Client(address, authkey=process.current_process().authkey)
     88         c.send((key, os.getpid()))
     89         return c

/usr/lib/python3.6/multiprocessing/connection.py in Client(address, family, authkey)
    491 
    492     if authkey is not None:
--> 493         answer_challenge(c, authkey)
    494         deliver_challenge(c, authkey)
    495 

/usr/lib/python3.6/multiprocessing/connection.py in answer_challenge(connection, authkey)
    730     import hmac
    731     assert isinstance(authkey, bytes)
--> 732     message = connection.recv_bytes(256)         # reject large message
    733     assert message[:len(CHALLENGE)] == CHALLENGE, 'message = %r' % message
    734     message = message[len(CHALLENGE):]

/usr/lib/python3.6/multiprocessing/connection.py in recv_bytes(self, maxlength)
    214         if maxlength is not None and maxlength < 0:
    215             raise ValueError("negative maxlength")
--> 216         buf = self._recv_bytes(maxlength)
    217         if buf is None:
    218             self._bad_message_length()

/usr/lib/python3.6/multiprocessing/connection.py in _recv_bytes(self, maxsize)
    405 
    406     def _recv_bytes(self, maxsize=None):
--> 407         buf = self._recv(4)
    408         size, = struct.unpack("!i", buf.getvalue())
    409         if maxsize is not None and size > maxsize:

/usr/lib/python3.6/multiprocessing/connection.py in _recv(self, size, read)
    377         remaining = size
    378         while remaining > 0:
--> 379             chunk = read(handle, remaining)
    380             n = len(chunk)
    381             if n == 0:

ConnectionResetError: [Errno 104] Connection reset by peer

I am using a Docker Image on a Linux system. Can you help me to get the notebook to run?

Bumps protobuf from 3.8.0 to 3.18.3.

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.

While I want to pip install ocr it is not working for me. What python version this was used for? How do I install the module ocr in Jupyter notebook. I tried pip install it doesn't work

We are unable to connect to IAM database , we went through given site , sign in but it says we have to write username and pass in jason file , we tried that too. If someone can help please reply

In README.MD links to Python scripts for each step are broken, i.e.

• for first step link is to https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/ocr/scripts/paragraph_segmentation_dcnn.py but should be https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/ocr/paragraph_segmentation_dcnn.py
• for second step it should be https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/ocr/word_and_line_segmentation.py
• for third step it should be https://github.com/awslabs/handwritten-text-recognition-for-apache-mxnet/blob/master/ocr/handwriting_line_recognition.py

I was trying to get the 4 testing images following the notebook, but when downloading the IAM images it says "ReadError: not a gzip file. Could anyone kindly help here ?