mindocr-copy/tools/dataset_converters

• Text Detection/Spotting Annotation
• Text Recognition Annotation

This document shows how to convert ocr annotation to the general format (not including LMDB) for model training.

Text Detection/Spotting Annotation

The format of the converted annotation file should follow:

img_61.jpg\t[{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}]   

To convert the ic15 dataset to the required format, please run

# convert training anotation
python tools/dataset_converters/convert.py \
        --dataset_name  ic15 \
        --task det \
        --image_dir /path/to/ic15/det/train/ch4_training_images \
        --label_dir /path/to/ic15/det/train/ch4_training_localization_transcription_gt \
        --output_path /path/to/ic15/det/train/det_gt.txt 

# convert testing anotation
python tools/dataset_converters/convert.py \
        --dataset_name  ic15 \
        --task det \
        --image_dir /path/to/ic15/det/test/ch4_test_images \
        --label_dir /path/to/ic15/det/test/ch4_test_localization_transcription_gt \
        --output_path /path/to/ic15/det/test/det_gt.txt 

Text Recognition Annotation

The annotation format for text recognition dataset follows

word_7.png	fusionopolis
word_8.png	fusionopolis
word_9.png	Reserve
word_10.png	CAUTION
word_11.png	citi

Note that image name and text label are seperated by \t.

To convert, please run:

# convert training anotation
python tools/dataset_converters/convert.py \
        --dataset_name  ic15 \
        --task rec \
        --label_dir /path/to/ic15/rec/ch4_training_word_images_gt/gt.txt
        --output_path /path/to/ic15/rec/train/ch4_training_word_images_gt/rec_gt.txt 

# convert testing anotation
python tools/dataset_converters/convert.py \
        --dataset_name  ic15 \
        --task rec \
        --label_dir /path/to/ic15/rec/ch4_test_word_images_gt/gt.txt
        --output_path /path/to/ic15/rec/ch4_test_word_images_gt/rec_gt.txt