【个人开源】论文复现SRN：Towards Accurate Scene Text Recognition with Semantic Reasoning Networks

Towards Accurate Scene Text Recognition with Semantic Reasoning Networks

Code:https://github.com/chenjun2hao/SRN.pytorch

Unofficial PyTorch implementation of the paper, which integrates not only global semantic reasoning module but also parallel visual attention module and visual-semantic fusion decoder.the semanti reasoning network(SRN) can be trained end-to-end.

At present, the accuracy of the paper cannot be achieved. And i borrowed code from deep-text-recognition-benchmark

model

result

IIIT5k_3000	SVT	IC03_860	IC03_867	IC13_857	IC13_1015	IC15_1811	IC15_2077	SVTP	CUTE80
84.600	83.617	92.907	92.849	90.315	88.177	71.010	68.064	71.008	68.641

total_accuracy: 80.597

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Feature

• predict the character at once time
• DistributedDataParallel training

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Requirements

Pytorch >= 1.1.0

Test

1. download the evaluation data from deep-text-recognition-benchmark

2. download the pretrained model from Baidu, Password: d2qn

3. test on the evaluation data

python test.py --eval_data path-to-data --saved_model path-to-model

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Train

1. download the training data from deep-text-recognition-benchmark

2. training from scratch

python train.py --train_data path-to-train-data --valid-data path-to-valid-data

Reference

1. bert_ocr.pytorch
2. deep-text-recognition-benchmark
3. 2D Attentional Irregular Scene Text Recognizer
4. Towards Accurate Scene Text Recognition with Semantic Reasoning Networks

difference with the origin paper

• use resnet for 1D feature not resnetFpn 2D feature
• use add not gated unit for visual-semanti fusion decoder

other

It is difficult to achieve the accuracy of the paper, hope more people to try and share