Towards Accurate Scene Text Recognition with Semantic Reasoning Networks
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
Feature
predict the character at once time
DistributedDataParallel training
Requirements
Pytorch >= 1.1.0
Test
download the evaluation data from deep-text-recognition-benchmark
download the pretrained model from Baidu , Password: d2qn
test on the evaluation data
python test.py --eval_data path-to-data --saved_model path-to-model
Train
download the training data from deep-text-recognition-benchmark
training from scratch
python train.py --train_data path-to-train-data --valid-data path-to-valid-data
Reference
bert_ocr.pytorch
deep-text-recognition-benchmark
2D Attentional Irregular Scene Text Recognizer
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
来源:https://www./content-4-693301.html