mirror of https://github.com/open-mmlab/mmocr.git
57 lines
3.6 KiB
Markdown
57 lines
3.6 KiB
Markdown
# Changelog
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## v0.2.0 (18/5/2021)
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**Highlights**
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1. Add the NER approach Bert-softmax (NAACL'2019)
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2. Add the text detection method DRRG (CVPR'2020)
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3. Add the text detection method FCENet (CVPR'2021)
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4. Increase the ease of use via adding text detection and recognition end-to-end demo, and colab online demo.
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5. Simplify the installation.
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**New Features**
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- Add Bert-softmax for Ner task [#148](https://github.com/open-mmlab/mmocr/pull/148)
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- Add DRRG [#189](https://github.com/open-mmlab/mmocr/pull/189)
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- Add FCENet [#133](https://github.com/open-mmlab/mmocr/pull/133)
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- Add end-to-end demo [#105](https://github.com/open-mmlab/mmocr/pull/105)
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- Support batch inference [#86](https://github.com/open-mmlab/mmocr/pull/86) [#87](https://github.com/open-mmlab/mmocr/pull/87) [#178](https://github.com/open-mmlab/mmocr/pull/178)
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- Add TPS preprocessor for text recognition [#117](https://github.com/open-mmlab/mmocr/pull/117) [#135](https://github.com/open-mmlab/mmocr/pull/135)
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- Add demo documentation [#151](https://github.com/open-mmlab/mmocr/pull/151) [#166](https://github.com/open-mmlab/mmocr/pull/166) [#168](https://github.com/open-mmlab/mmocr/pull/168) [#170](https://github.com/open-mmlab/mmocr/pull/170) [#171](https://github.com/open-mmlab/mmocr/pull/171)
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- Add checkpoint for Chinese recognition [#156](https://github.com/open-mmlab/mmocr/pull/156)
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- Add metafile [#175](https://github.com/open-mmlab/mmocr/pull/175) [#176](https://github.com/open-mmlab/mmocr/pull/176) [#177](https://github.com/open-mmlab/mmocr/pull/177) [#182](https://github.com/open-mmlab/mmocr/pull/182) [#183](https://github.com/open-mmlab/mmocr/pull/183)
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- Add support for numpy array inference [#74](https://github.com/open-mmlab/mmocr/pull/74)
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**Bug Fixes**
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- Fix the duplicated point bug due to transform for textsnake [#130](https://github.com/open-mmlab/mmocr/pull/130)
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- Fix CTC loss NaN [#159](https://github.com/open-mmlab/mmocr/pull/159)
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- Fix error raised if result is empty in demo [#144](https://github.com/open-mmlab/mmocr/pull/141)
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- Fix results missing if one image has a large number of boxes [#98](https://github.com/open-mmlab/mmocr/pull/98)
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- Fix package missing in dockerfile [#109](https://github.com/open-mmlab/mmocr/pull/109)
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**Improvements**
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- Simplify installation procedure via removing compiling [#188](https://github.com/open-mmlab/mmocr/pull/188)
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- Speed up panet post processing so that it can detect dense texts [#188](https://github.com/open-mmlab/mmocr/pull/188)
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- Add zh-CN README [#70](https://github.com/open-mmlab/mmocr/pull/70) [#95](https://github.com/open-mmlab/mmocr/pull/95)
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- Support windows [#89](https://github.com/open-mmlab/mmocr/pull/89)
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- Add Colab [#147](https://github.com/open-mmlab/mmocr/pull/147) [#199](https://github.com/open-mmlab/mmocr/pull/199)
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- Add 1-step installation using conda environment [#193](https://github.com/open-mmlab/mmocr/pull/193) [#194](https://github.com/open-mmlab/mmocr/pull/194) [#195](https://github.com/open-mmlab/mmocr/pull/195)
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## v0.1.0 (7/4/2021)
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**Highlights**
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- MMOCR is released.
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**Main Features**
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- Support text detection, text recognition and the corresponding downstream tasks such as key information extraction.
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- For text detection, support both single-step (`PSENet`, `PANet`, `DBNet`, `TextSnake`) and two-step (`MaskRCNN`) methods.
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- 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`.
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- For key information extraction, support GCN based method `SDMG-R`.
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- Provide checkpoints and log files for all of the methods above.
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