text-detection-ctpn-pytorch

my blog about CTPN blog

Conlusion

Ctpn works better for horizontal text, but worse for oblique and curved text.If you are interested in text detection and model compression, you can pay attention to the two projects I will do next, psenet and DBNet

setup

nms and bbox utils are written in cython, you have to build the library first.

cd utils/bbox
sh make.sh

It will generate a nms.so and a bbox.so in current folder.

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how to test

• follow setup to build the library
• download the test model
• change you own model_path , dir_path and save_path in inference.py

python3 inference.py

test model

base_model	Model_size(M)	model_file
vgg16_bn	67.7	baiduyun(extract code: 5pgy)
resnet50	137	baiduyun(extract code: 5pgy)
shufflenet_v2_x1_0	25.4	baiduyun (extract code: 5pgy)
mobilenet_v3_large	16.9	baiduyun (extract code: 5pgy)
mobilenet_v3_small	13.5	baiduyun (extract code: 5pgy)

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how to train

data format

follow icdar15 dataset format, x1,y1,x2,y2,x3,y3,x4,y4,label

image
│   1.jpg
│   2.jpg   
│		...
label
│   1.txt
│   2.txt
|		...

train

Simplely run

python3 train.py --base_model vgg16_bn --batch_size 4 --size_list [1048]

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Some explanations

1. Support switching basemodel,(mobilenet_v3_large,mobilenet_v3_small, shufflenet_v2_x1_0, shufflenet_v2_x0_5, vgg11, vgg11_bn, vgg16, vgg16_bn, vgg19, vgg19_bn, resnet18, resnet34 ,resnet50, resnet101, resnet152)
2. Ohem algorithm is added
3. Support batch training
4. When the size_list has multiple values, the maximum edge of the training picture will be randomly zoomed when training. It should be noted that you must ensure that your GPU memory supports maximum edge scaling.

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performance

only train on icdar2015

rescall	prediction	hmean
0.4058	0.6117	0.4879

some results in icdar2015

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some results in MTWI2018

reference

1. https://github.com/eragonruan/text-detection-ctpn
2. https://github.com/AstarLight/Lets_OCR/tree/master/detector/ctpn
3. https://github.com/xhzdeng/stela
4. https://github.com/xiaolai-sqlai/mobilenetv3