First of all, i would like to thank Jieru Mei. My repo was heavily based on his repo.
This repo implemented convolution recurrent neural nets(CRNN) for handwritten recognition, optical character recognition. CRNN can use for many text levels: character, word, or even a text line.
In this repo, I try to provide a simple API which help everyone can train their own model. This model, which i have used for many industry projects, can guarantee to work well in many cases.
You must organize your dataset in the below structure
├── 23963.jpg
├── 23963.txt
├── char
├── test
└── train
Here you must put your images with it's labels in the same folder. Each image is a text line, so just put only one line of text in corresponding label file.
train: list of all training files
test: list of all testing files
char: list of all characters in labels
You can look at data folder to see an example.
There are critical options which you need to modify to adapt you dataset's properties:
- imgW: should be larger than the maximun width of images in training set. All images, which have small width, will be padded in the right hand.
- imgH: should be 64 in the model since the downsampling rate is 64 pixel in vertical orientation.
Training process was simplified, just type the command in your terminal and you should use the pretrain model
python train.py --root {root_folder} --train train --val test --alphabet char --pretrain {pretrain_model} --imgW {your_width} --imgH 64 --pretrain expr/crnn.rnn.pth
To use GPU to speed up your training, just pass --cuda in options. Your trained model will be exported your --save_model in options. Please see the code for more options.
To make a prediction for a new image
python demo.py --model {model} --alphabet {char} --imgW {width} --imgH 32 --img {img}
Here is a sample
Result: đđ-ặ--nngg vvviiệệtt tt--h--ắ--nnggg ----> đặng việt thắng : prob: 0.9896178245544434
If you have any problems, please email me or create an issue.