Changelog

v0.2.0 (18/5/2021)

Highlights

1. Add the NER approach Bert-softmax (NAACL’2019)

2. Add the text detection method DRRG (CVPR’2020)

3. Add the text detection method FCENet (CVPR’2021)

4. Increase the ease of use via adding text detection and recognition end-to-end demo, and colab online demo.

5. Simplify the installation.

New Features

• Add Bert-softmax for Ner task #148

• Add DRRG #189

• Add FCENet #133

• Add end-to-end demo #105

• Support batch inference #86 #87 #178

• Add TPS preprocessor for text recognition #117 #135

• Add demo documentation #151 #166 #168 #170 #171

• Add checkpoint for Chinese recognition #156

• Add metafile #175 #176 #177 #182 #183

• Add support for numpy array inference #74

Bug Fixes

• Fix the duplicated point bug due to transform for textsnake #130

• Fix CTC loss NaN #159

• Fix error raised if result is empty in demo #144

• Fix results missing if one image has a large number of boxes #98

• Fix package missing in dockerfile #109

Improvements

• Simplify installation procedure via removing compiling #188

• Speed up panet post processing so that it can detect dense texts #188

• Add zh-CN README #70 #95

• Support windows #89

• Add Colab #147 #199

• Add 1-step installation using conda environment #193 #194 #195

v0.1.0 (7/4/2021)

Highlights

• MMOCR is released.

Main Features

• Support text detection, text recognition and the corresponding downstream tasks such as key information extraction.

• For text detection, support both single-step (PSENet, PANet, DBNet, TextSnake) and two-step (MaskRCNN) methods.

• For text recognition, support CTC-loss based method CRNN; Encoder-decoder (with attention) based methods SAR, Robustscanner; Segmentation based method SegOCR; Transformer based method NRTR.

• For key information extraction, support GCN based method SDMG-R.

• Provide checkpoints and log files for all of the methods above.