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Background
MindOCR currently does not support end-to-end detection and recognition models, I hope to solve this problem by contributing a PGNet model.
Model Introduction
Existing text recognizers are mostly built on two-stage frameworks or character-based methods, which suffer from non-maximum suppression (NMS), region of interest (RoI) operations or character-level annotations. To solve the above problems, PGNets uses a novel fully convolutional point collection network for reading arbitrary-shaped text in real time.
Background
MindOCR currently does not support end-to-end detection and recognition models, I hope to solve this problem by contributing a PGNet model.
Model Introduction
Existing text recognizers are mostly built on two-stage frameworks or character-based methods, which suffer from non-maximum suppression (NMS), region of interest (RoI) operations or character-level annotations. To solve the above problems, PGNets uses a novel fully convolutional point collection network for reading arbitrary-shaped text in real time.
Reference Papers
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network Pengfei Wang, Chengquan Zhang, FeiQi, Shanshan Liu, Xiaoqiang Zhang, Pengyuan Lyu, Junyu Han, Jingtuo Liu, Errui Ding, Guangming Shi AAAI, 2021
Expectations
I'd like to get the community's comments, suggestions, and related reference documents or PRs.
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