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