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purpose This code is written by Koichiro Tamura in order to understand Residual Attention Network for Image Classification. explanation I made document which explains Residual Attention Network for Image Classification(in Japanese). The document would be on SlideShare and the website of Deep Learning JP how to use Requirements Anaconda3.x tensorflow 1.x keras 2.x train $ python residual-attention-network/train.py test Sorry, I have not written scripts for test.

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text_segment_deeplearning_based's Introduction

text_segment_deeplearning_based

purpose

This code is written by tengshaofeng in order to segment character in a text line image.

reference paper

Chinese/English mixed Character Segmentation as Semantic Segmentation. Huabin Zheng

Requirements

Anaconda2.x tensorflow 0.x

prepare dataset

you can generate the dataset from several types of fonts by run: python dataset_gen.py the gen_imgs folder store the images, and corpus.txt store the characters. also you can manually label the 1024*48 text line image. dataset.txt save the label like： ['0.png', 0, 36, 45, 82, 87, 126, 129, 167, 172, 209, 215, 252, 253, 290, 305, 341, 354, 392, 393, 433, 435, 473] ['1.png', 11, 47, 56, 94, 99, 135, 141, 177, 179, 218, 232, 268, 273, 311, 326, 365, 368, 408, 421, 461, 461, 5] 0 , 36, 45 and so on is the split points(cols) in the image.

train

python CharacterSegmentTrain.py

experiment result

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berryhn / text_segment_deeplearning_based Goto Github PK