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Attention-OCR

Bidirectional LSTM encoder and attention-enhanced GRU decoder stacked on a multilayer CNN for image-to-transcription.

This repository is associated with the paper "Character-Based Handwritten Text Transcription with Attention Networks".

Please cite the paper if you use this code for academic research:

@article{DBLP:journals/corr/abs-1712-04046,
  author    = {Jason Poulos and
               Rafael Valle},
  title     = {Character-Based Handwritten Text Transcription with Attention Networks},
  journal   = {CoRR},
  volume    = {abs/1712.04046},
  year      = {2017},
  url       = {http://arxiv.org/abs/1712.04046},
  archivePrefix = {arXiv},
  eprint    = {1712.04046},
  timestamp = {Mon, 13 Aug 2018 16:47:16 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1712-04046},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Acknowledgements

This repo is forked from Attention-OCR by Qi Guo and Yuntian Deng. The model is described in their paper What You Get Is What You See: A Visual Markup Decompiler.

IAM image and transcription preprocessing from Laia.

Prerequsites

Python 3 (tested on Python 3.6.6)

Tensorflow 1 (tested on 1.13.1)

Required packages: {distance, tqdm, pillow, matplotlib, imgaug}:

pip3 install {package}

Image-to-transcription on IAM:

Data Preparation

Follow steps for IAM data preparation. IAM consists of approx. 10k images of handwritten text lines and their transcriptions. The code in the linked repo binarizes the images in a manner that preserves the original grayscale information, converts to JPEG, and scales to 64 pixel height. The code creates a folder for preprocessed images imgs_proc and transcriptions htr/lang/char.

IAM original IAM preprocessed

Create a file lines_train.txt from the transcription tr.txt that replaces whitespace with a vertical pipe and contains the path of images and the corresponding characters, e.g.:

./imgs_proc/a01-000u-00.jpg A|MOVE|to|stop|Mr.|Gaitskell|from
./imgs_proc/a01-000u-01.jpg nominating|any|more|Labour|life|Peers
./imgs_proc/a01-000u-02.jpg is|to|be|made|at|a|meeting|of|Labour

Also create files lines_val.txt and lines_test.txt from htr/lang/word/va.txt and htr/lang/word/te.txt, respectively, following the same format as above.

Assume that the working directory is Attention-OCR. The data files within Attention-OCR should have the structure:

  • src
  • iamdb
    • imgs_proc (folder of JPEG images)
    • lines_train.txt
    • lines_val.txt
    • lines_test.txt

Train

python src/launcher.py \
--phase=train \
--data-path=lines_train.txt \
--data-base-dir=iamdb \
--model-dir=model_iamdb_softmax \
--log-path=log_iamdb_train_softmax.txt \
--reg-val=0.001 \
--attn-num-hidden=256 \
--attn-num-layers=2 \
--batch-size=8 \
--num-epoch=200 \
--steps-per-checkpoint=500 \
--opt-attn=softmax \
--target-embedding-size=5 \
--target-vocab-size=124 \
--initial-learning-rate=0.5 \
--augmentation=0.1 \
--gpu-id=0 \
--load-model

You will see something like the following output in log_iamdb_train.txt:

...
09:22:22,993 root  INFO     Created model with fresh parameters.
2020-02-18 09:22:59,658 root  INFO     Generating first batch
2020-02-18 09:23:03,393 root  INFO     current_step: 0
2020-02-18 09:24:33,511 root  INFO     step 0.000000 - time: 90.118267, loss: 4.375765, perplexity: 79.500660, precision: 0.020499, CER: 0.979798, batch_len: 469.000000
2020-02-18 09:24:34,033 root  INFO     current_step: 1
2020-02-18 09:24:34,677 root  INFO     step 1.000000 - time: 0.644488, loss: 4.364702, perplexity: 78.625946, precision: 0.013305, CER: 0.986486, batch_len: 301.000000
2020-02-18 09:24:35,224 root  INFO     current_step: 2
2020-02-18 09:24:35,955 root  INFO     step 2.000000 - time: 0.731375, loss: 4.341702, perplexity: 76.838169, precision: 0.114527, CER: 0.889571, batch_len: 613.000000
2020-02-18 09:24:36,010 root  INFO     current_step: 3
2020-02-18 09:24:36,721 root  INFO     step 3.000000 - time: 0.713290, loss: 4.327676, perplexity: 75.768019, precision: 0.169855, CER: 0.830409, batch_len: 516.000000
2020-02-18 09:24:36,824 root  INFO     current_step: 4
2020-02-18 09:24:37,508 root  INFO     step 4.000000 - time: 0.686172, loss: 4.304539, perplexity: 74.035057, precision: 0.165195, CER: 0.836158, batch_len: 457.000000
2020-02-18 09:24:37,706 root  INFO     current_step: 5
2020-02-18 09:24:38,399 root  INFO     step 5.000000 - time: 0.694256, loss: 4.264017, perplexity: 71.095007, precision: 0.192181, CER: 0.805128, batch_len: 481.000000

Model checkpoints saved in model_iamdb_softmax .

Test model and visualize attention

We provide a trained model on IAM:

wget https://www.dropbox.com/s/vq77vehdexnioow/model_iamdb_softmax_124500.tar.gz
tar -xvzf model_iamdb_softmax_124500.tar.gz
python3 src/launcher.py \
--phase=test \
--visualize \
--data-path=lines_test.txt \
--data-base-dir=iamdb \
--model-dir=model_iamdb_softmax \
--log-path=log_iamdb_test.txt \
--reg-val=0.001 \
--attn-num-hidden=256 \
--attn-num-layers=2 \
--batch-size=8 \
--num-epoch=200 \
--steps-per-checkpoint=500 \
--opt-attn=softmax \
--target-embedding-size=5 \
--target-vocab-size=124 \
--initial-learning-rate=0.5 \
--augmentation=0.1 \
--gpu-id=0 \
--load-model \
--output-dir=softmax_results

You will see something like the following output in log_iamdb_test.txt:

2017-05-04 20:06:32,116 root  INFO     Reading model parameters from model_iamdb_softmax/translate.ckpt-731000
2017-05-04 20:09:54,266 root  INFO     Compare word based on edit distance.
2017-05-04 20:09:57,299 root  INFO     step_time: 2.684323, loss: 12.952633, step perplexity: 421946.118697
2017-05-04 20:10:10,894 root  INFO     0.489362 out of 1 correct
2017-05-04 20:10:11,710 root  INFO     step_time: 0.779765, loss: 16.425102, step perplexity: 13593499.165457
2017-05-04 20:10:22,828 root  INFO     0.771970 out of 2 correct
2017-05-04 20:10:23,627 root  INFO     step_time: 0.776458, loss: 20.803520, step perplexity: 1083562653.786069
2017-05-04 20:10:47,098 root  INFO     1.423133 out of 3 correct
2017-05-04 20:10:48,040 root  INFO     step_time: 0.918638, loss: 11.657264, step perplexity: 115527.486132
2017-05-04 20:11:04,398 root  INFO     2.246663 out of 4 correct
2017-05-04 20:11:07,883 root  INFO     step_time: 3.448558, loss: 10.126567, step perplexity: 24998.394628
2017-05-04 20:11:25,554 root  INFO     2.483505 out of 5 correct

Output images in softmax_results (the output directory is set via parameter output-dir and the default is results). This example visualizes attention on an image:

demo

This example plots the attention alignment over an image:

demo

Parameters:

Default parameters set in the file src/exp_config.py.

  • Control

    • GPU-ID: ID number of the GPU. Default is 0.
    • phase: Determine whether to 'train' or 'test'. Default is 'test'.
    • visualize: Valid if phase is set to test. Output the attention maps on the original image. Set flag to no-visualize to test without visualizing.
    • load-model: Load model from model-dir or not.
    • target-vocab-size: Target vocabulary size. Default is = 26+10+3 # 0: PADDING, 1: GO, 2: EOS, >2: 0-9, a-z
  • Input and output

    • data-base-dir: The base directory of the image path in data-path. If the image path in data-path is absolute path, set it to /.
    • data-path: The path containing data file names and labels. Format per line: image_path characters.
    • model-dir: The directory for saving and loading model parameters (structure is not stored). Default is 'train'.
    • log-path: The path to put log. Default is 'log.txt'
    • output-dir: The path to put visualization results if visualize is set to True. Default is 'results'.
    • steps-per-checkpoint: Checkpointing (print perplexity, save model) per how many steps. Default is 500.
    • augmentation: P(data augmentation). Default is 0.2.
  • Optimization

    • num-epoch: The number of whole data passes. Default is 1000.
    • batch-size: Batch size. Only valid if phase is set to train. Default is 64.
    • initial-learning-rate: Initial (AdaDelta) learning rate. Default is 1.
  • Network

    • reg-val: Lambda for L2 regularization losses. Default is 0.
    • clip-gradients: Whether to perform gradient clipping. Default is 'True'.
    • max-gradient-norm: Clip gradients to this norm. Default is 5.
    • target-embedding-size: Embedding dimension for each target. Default is 10.
    • opt-attn: Which attention mechanism to use: 'softmax' (default); 'log_softmax'; 'sigmoid'; 'no_attn'.
    • use-gru: Use GRU for decoder (rather than LSTM). Default is 'True'.
    • attn-num-hidden: Number of hidden units in attention decoder cell. Default is 128.
    • attn-num-layers: Number of layers in attention decoder cell. Default is 2. (Encoder number of hidden units will be attn-num-hidden*attn-num-layers).

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