This repository includes the implementation of the method from our paper. It is implemented via tensorflow 1.12.0. Please use the following citation.

@inproceedings{dong2018multi, title={Multi-Input Attention for Unsupervised OCR Correction}, author={Dong, Rui and Smith, David}, booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, volume={1}, pages={2363--2372}, year={2018} }

A trained model for the richmond dispatch newspaper data could be find here. And the corresponding vocabulary could be find here. The model contains 3 layers and the hidden size is 512. Please change the model path to your own path to store the model in the "checkpoint" file.

It contains the following file:

• Process the input files:
  • Parameters:
    • data_dir: the directory storing input json files that contains OCR output with their manual transcription and wintesses.
    • out_dir: the output directory
  • Input: It takes the json files in DATA_DIR as input.
  • Output:
    • OUT_DIR/pair.x, it contains the OCR'd Output, each line corresponds to a line in the original image.
    • OUT_DIR/pair.x.info, it contains the infomation for each OCR'd line split by tab:(group no., line no., file_id, begin index in file, end index in file, number of witnesses, number of manual transcriptions)
    • OUT_DIR/pair.y, each line corresponds to the witnesses of one line in file "pair.x" split by tab('\t')
    • OUT_DIR/pair.y.info, it contains the information for each witness line in 'pair.y' split by tab: (line_no, file_id, begin index in file). If "line no." = 100 for the 10th line in "pair.y.info", it means that the 10th line of "pair.y" contains the witnesses of the 101th line of file "pair.x".
    • OUT_DIR/pair.z, each line corresponds to the manual transcription of one line in file "pair.x" split by tab('\t')
    • OUT_DIR/pair.z.info, it contains the information for each witness line in 'pair.y' split by tab: (line_no, file_id, begin index in file). If "line no." = 100 for the 10th line in "pair.z.info", it means that the 10th line of "pair.z" contains the manual transcription of the 101th line of file "pair.x".

• Generate the supervised training data:
  • Parameters:
    • data_dir: the directory storing the output from data_process.py.
    • out_dir: the output directory for the training, development and test data.
    • train_ratio: the ratio to split the training, development and test data.
  • Input: the output files from data_process.py
  • Output: OUT_DIR/train.x.txt, OUT_DIR/train.y.txt, OUT_DIR/dev.x.txt, OUT_DIR/dev.y.txt, OUT_DIR/test.x.txt, OUT_DIR/test.y.txt. Here files with postfix '.x.txt' are the OCR output and files with postfix '.y.txt' are the manual transcription.

• create the vocabulary
  • Parameters:
    • data_dir: the directory where your training data is stored.
    • prefix: the prefix of your file, e.g., train, dev, test, here we set it as train to generate the vocabulary with the training files.
    • gen_voc: True for generating a new vocabulary file, False for tokenizing the given files with a existing vocabulary, here we set it as True.
  • INPUT: It takes DATA_DIR/train.x.txt and DATA_DIR/train.y.txt for creating the vocabulary, each line in "train.x.txt" from OCR ou/tput, and each line in "train.y.txt" is the manually transcribed target for the corresponding line in "train.x.txt"
  • OUTPUT: DATA_DIR/vocab.dat the vocabulary file where each line is a character.
• tokenize a given file:
  • Parameters:
    • data_dir: the directory where the file your want to tokenize is stored
    • voc_dir: the directory where your vocabulary file is stored
    • prefix: the prefix of your files to be tokenized, e.g., "train", "dev", "test", here we set it as "train" to generate the vocabulary.
    • gen_voc: set it as False for tokenzing the given files with a exisiting vocabulary file
  • INPUT: It takes DATA_DIR/PREFIX.x.txt and DATA_DIR/PREFIX.y.txt as input and tokenize them with the given vocabulary file
  • OUTPUT: DATA_DIR/PREFIX.ids.x and DATA_DIR/PREFIX.ids.y, the tokenized files where each line is the id of each character for the line in the corresponding input file

It is an attention-based seq2seq model modified based on the neural language correction model.

• Basic Parameters:
  • data_dir: the directory of training and development files
  • voc_dir: the directory of the vocabulary file
  • train_dir: the directory to store the trained model
  • num_layers: number of layers of LSTM
  • size: the hidden size of LSTM unit
• INPUT: It takes the tokenized training files DATA_DIR/train.ids,x, DATA_DIR/train.ids.y and development files DATA_DIR/dev.ids.x, DATA_DIR/dev.y.ids as well as the vocabulary file as input, train the model on the training files and evaluate the model on the development files to decide whether to store a new checkpoint.
• OUTPUT: A correction model.

• Basic Parameters:
  • data_dir: the directory of test file
  • voc_dir: the directory of the vocabulary file
  • train_dir: the directory where the trained model is stored
  • out_dir: the directory to store the output files
  • num_layers: number of layers of LSTM
  • size: the hidden size of LSTM unit
  • decode: the decoding strategy to use: single, average, weight, flat
  • beam_size: beam search width
• INPUT: It takes the test files DATA_DIR/test.x.txt, DATA_DIR/test.y.txt, the vocabulary file VOC_DIR/vocab.dat, and the trained model TRAIN_DIR/best-ckpt as input for decoding.
• OUTPUT: It output the decoding results in two files:
  • OUT_DIR/test.DECODE.o.txt: storing the top BEAM_SIZE decoding suggestions for each line in test file. It has N * BEAM_SIZE lines, every line in test.x.txt corresponds to BEAM_SIZE lines in this file.
  • OUT_DIR/test.DECODE.p.txt: storing the probability of each decoding suggestion in the above file.