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Project structure

assets/                       # assets (see description below)
manga_ocr/                    # release code (inference only)
manga_ocr_dev/                # development code
   env.py                     # global constants
   data/                      # data preprocessing
   synthetic_data_generator/  # generation of synthetic image-text pairs
   training/                  # model training

assets

fonts.csv

csv with columns:

  • font_path: path to font file, relative to FONTS_ROOT
  • supported_chars: string of characters supported by this font
  • num_chars: number of supported characters
  • label: common/regular/special (used to sample regular fonts more often than special)

List of fonts with metadata used by synthetic data generator. Provided file is just an example, you have to generate similar file for your own set of fonts, using manga_ocr_dev/synthetic_data_generator/scan_fonts.py script. Note that label will be filled with regular by default. You have to label your special fonts manually.

lines_example.csv

csv with columns:

  • source: source of text
  • id: unique id of the line
  • line: line from language corpus

Example of csv used for synthetic data generation.

len_to_p.csv

csv with columns:

  • len: length of text
  • p: probability of text of this length occurring in manga

Used by synthetic data generator to more-or-less match the natural distribution of text lengths. Computed based on Manga109-s dataset.

vocab.csv

List of all characters supported by tokenizer.

Training OCR

env.py contains global constants used across the repo. Set your paths to data etc. there.

  1. Download Manga109-s dataset.
  2. Set MANGA109_ROOT, so that your directory structure looks like this: 
    <MANGA109_ROOT>/
    Manga109s_released_2021_02_28/
        annotations/
        annotations.v2018.05.31/
        images/
        books.txt
        readme.txt
  3. Preprocess Manga109-s with data/process_manga109s.py
  4. Optionally generate synthetic data (see below)
  5. Train with manga_ocr_dev/training/train.py

Synthetic data generation

Generated data is split into packages (named 0000, 0001 etc.) for easier management of large dataset. Each package is assumed to have similar data distribution, so that a properly balanced dataset can be built from any subset of packages.

Data generation pipeline assumes following directory structure:

<DATA_SYNTHETIC_ROOT>/
   img/           # generated images (output from generation pipeline)
      0000/
      0001/
      ...
   lines/         # lines from corpus (input to generation pipeline)
      0000.csv
      0001.csv
      ...
   meta/          # metadata (output from generation pipeline)
      0000.csv
      0001.csv
      ...

To use a language corpus for data generation, lines/*.csv files must be provided. For a small example of such file see assets/lines_example.csv.

To generate synthetic data:

  1. Generate backgrounds with data/generate_backgrounds.py.
  2. Put your fonts in <FONTS_ROOT>.
  3. Generate fonts metadata with synthetic_data_generator/scan_fonts.py.
  4. Optionally manually label your fonts with common/regular/special labels.
  5. Provide <DATA_SYNTHETIC_ROOT>/lines/*.csv.
  6. Run synthetic_data_generator/run_generate.py for each package.