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Unofficial PyTorch implementation of 2D Attentional Irregular Scene Text Recognizer

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2D Attentional Irregular Scene Text Recognizer

Unofficial PyTorch implementation of the paper, which transforms the irregular text with 2D layout to character sequence directly via 2D attentional scheme. They utilize a relation attention module to capture the dependencies of feature maps and a parallel attention module to decode all characters in parallel.

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

model

result
Test on ICDAR2019 with only 51.15%, will continue to improve.

Feature

  1. Output image string once not like the seqtoseq model

Requirements

Pytorch >= 1.1.0

Test

  1. download the pretrained model Baidu password: kdah.

  2. test on images which in demo_image folder

python demo.py --image_folder demo_image --saved_model <model_path/best_accuracy.pth>
  1. some examples
demo images Bert_OCR
available
shakesshack
london
greenstead
toast
merry
underground
ronaldo
bally
university
  1. result on benchmark data sets
IIIT5k_3000 SVT IC03_860 IC03_867 IC13_857 IC13_1015 IC15_1811 IC15_2077 SVTP CUTE80
84.367 79.907 91.860 91.465 88.448 86.010 65.654 63.215 68.527 81.185

total_accuracy: 78.423


Train

  1. I prepared a small dataset for train.The image and labels are in ./dataset/BAIDU.
python train.py --root ./dataset/BAIDU/images/ --train_csv ./dataset/BAIDU/small_train.txt --val_csv ./dataset/BAIDU/small_train.txt

Reference

  1. deep-text-recognition-benchmark
  2. 2D Attentional Irregular Scene Text Recognizer

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Unofficial PyTorch implementation of 2D Attentional Irregular Scene Text Recognizer

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