【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
【个人开源】论文复现SRN:Towards Accurate Scene Text Recognition with Semantic Reasoning Networks_第1张图片

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


Feature

  • predict the character at once time
  • DistributedDataParallel training

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

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

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