mercurial24 / bert_ocr.pytorch

Unofficial PyTorch implementation of 2D Attentional Irregular Scene Text Recognizer

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

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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>

3. some examples

demo images	Bert_OCR
	available
	shakesshack
	london
	greenstead
	toast
	merry
	underground
	ronaldo
	bally
	university

4. 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

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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