yolo3 + densenet + ctc ocr

see setup

• densenet model used for 5990 chars
  url：https://pan.baidu.com/s/1gm0Uq_sLe00En-IbUPiQUg password ：qcco
  put the model file in project_root/chinese_ocr/models/densenet_base_model/1

• densenet model used for 7476 chars
  url：https://pan.baidu.com/s/1_eGdF9odvzziJn35wOzQlA password ：jve5 put the model file in project_root/chinese_ocr/models/densenet_base_model/2

• other model url: https://pan.baidu.com/s/10t5BYHm-YJXb9NpT7OnIOg
  password: 8zbx
  put the model file in project_root/chinese_ocr/models/

目前提供的模型只适合学习使用，只用当前代码在生成的数据集上训练了很多轮保存的最好的一个版本，但不足以商用， 你可以自己用代码训练更好的模型，参考白翔老师的crnn也是个不错的选择

python demo.py

you can also see understand_detect

cd train

python train.py or you can use train_with_param to deal with different dataset

 ---dataset
    --images
        --xxx.jpg
    --data_train.txt
    --data_test.txt

this dataset is generate by code.

link：https://pan.baidu.com/s/1JgS1gSRcfnjWF_epU-E2vA password：wigu

The dataset contains 800,000 pictures 300,000 from chinese novel
100,000 from random number 0-9
100,000 from random code
300,000 random selected by it's frequency

• Random char space
• Random font size
• 10 different fonts
• Blur
• noise(gauss,uniform,salt_pepper,poisson)
• ...

for more detial see train_with_param

Or you can use YCG09's dataset to train,url:

url：https://pan.baidu.com/s/1QkI7kjah8SPHwOQ40rS1Pw (passwd：lu7m)

put your dataset into train/images and change the label file data_test.txt data_train.txt

or you can generate your own dataset:

• text location:
  SynthText
• text recognition
  TextRecognitionDataGenerator
  text_renderer (which one I used )
  you can use tools/tmp_label_to_id_label.py to change label file format to what we need here

1. use pretrain model to detect word

   • add demo √
   • add densenet training code √
   • test gpu nms √
   • generate my own dataset √

2. add framework to easy train on your own dataset

   • add yolo3 train code
   • make the code can be easy use on other dataset

https://github.com/chineseocr/chineseocr https://github.com/YCG09/chinese_ocr