cnocr
A python package for Chinese OCR with available trained models. So it can be used directly after installed.
The accuracy of the current crnn model is about 98.7%.
The project originates from our own (爱因互动 Ein+) internal needs. Thanks for the internal supports.
Changes
Most of the codes are adapted from crnn-mxnet-chinese-text-recognition. Much thanks to the author.
Some changes are:
- use raw MXNet CTC Loss instead of WarpCTC Loss. No more complicated installation.
- public pre-trained model for anyone. No more a-few-days training.
- add online
predictfunction and script. Easy to use.
Installation
pip install cnocr
Please use Python3 (3.4, 3.5, 3.6 should work). Python2 is not tested.
Usage
Predict
from cnocr import CnOcr
ocr = CnOcr()
res = ocr.ocr_for_single_line('examples/rand_cn1.png')
print("Predicted Chars:", res)
When you run the previous codes, the model files will be downloaded automatically from
Dropbox to ~/.cnocr.
The zip file will be extracted and you can find the resulting model files in ~/.cnocr/models by default.
In case the automatic download can't perform well, you can download the zip file manually
from Baidu NetDisk with extraction code pg26,
and put the zip file to ~/.cnocr. The code will do else.
Try the predict command for examples/rand_cn1.png:
python scripts/cnocr_predict.py --file examples/rand_cn1.png
You will get:
Predicted Chars: ['笠', '淡', '嘿', '骅', '谧', '鼎', '皋', '姚', '歼', '蠢', '驼', '耳', '胬', '挝', '涯', '狗', '蒽', '子', '犷']
(No NECESSARY) Train
You can use the package without any train. But if you really really want to train your own models, follow this:
python scripts/cnocr_train.py --cpu 2 --num_proc 4 --loss ctc --dataset cn_ocr
Future Work
- Support space recognition
- Bugfixes
- Add Tests
- Maybe use no symbol to rewrite the model
- Try other models such as DenseNet, ResNet
1 maintainer
Metadata
1 maintainer
Release files for cnocr 0.1.1
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