cnocr 0.2.0

Package for Chinese OCR, which can be used after installed without training yourself OCR model

中文版说明请见中文README。

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 predict function 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('examples/multi-line_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/multi-line_cn1.png:

python scripts/cnocr_predict.py --file examples/multi-line_cn1.png

You will get:

Predicted Chars: [['网', '络', '支', '付', '并', '无', '本', '质', '的', '区', '别', '，', '因', '为'], ['每', '一', '个', '手', '机', '号', '码', '和', '邮', '件', '地', '址', '背', '后'], ['都', '会', '对', '应', '着', '一', '个', '账', '户', '一', '一', '这', '个', '账'], ['户', '可', '以', '是', '信', '用', '卡', '账', '户', '、', '借', '记', '卡', '账'], ['户', '，', '也', '包', '括', '邮', '局', '汇', '款', '、', '手', '机', '代'], ['收', '、', '电', '话', '代', '收', '、', '预', '付', '费', '卡', '和', '点', '卡'], ['等', '多', '种', '形', '式', '。']]

Predict for Single-line-characters Image

If you know your image includes only one single line characters, you can use function Cnocr.ocr_for_single_line() instead of Cnocr.ocr(). Cnocr.ocr_for_single_line() should be more efficient.

from cnocr import CnOcr
ocr = CnOcr()
res = ocr.ocr_for_single_line('examples/rand_cn1.png')
print("Predicted Chars:", res)

With file examples/multi-line_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 multi-line-characters recognition
• Support space recognition
• Bugfixes
• Add Tests
• Maybe use no symbol to rewrite the model
• Try other models such as DenseNet, ResNet