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中文版说明请见中文README。
cnocr
v1.0.0
is released, which is more efficient for prediction. The new version of the model is not compatible with the previous version. So if upgrading, please download the latest model file again. See below for the details (same as before).
Main changes are:
train accuracy
always 0
.mxnet
is upgraded from 1.3.1
to 1.4.1
.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 98.8%
.
The project originates from our own (爱因互动 Ein+) internal needs. Thanks for the internal supports.
Most of the codes are adapted from crnn-mxnet-chinese-text-recognition. Much thanks to the author.
Some changes are:
predict
function and script. Easy to use.pip install cnocr
Please use Python3 (3.4, 3.5, 3.6 should work). Python2 is not tested.
The first time cnocr is used, 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 ss81
, and put the zip file to ~/.cnocr
. The code will do else.
Three functions are provided for prediction.
CnOcr.ocr(img_fp)
The function cnOcr.ocr (img_fp)
can recognize texts in an image containing multiple lines of text (or single lines).
Function Description
img_fp
: image file path; or color image mx.nd.NDArray
or np.ndarray
, with shape (height, width, 3)
, and the channels should be RGB formatted.List(List(Char))
, such as: [['第', '一', '行'], ['第', '二', '行'], ['第', '三', '行']]
.Use Case
from cnocr import CnOcr
ocr = CnOcr()
res = ocr.ocr('examples/multi-line_cn1.png')
print("Predicted Chars:", res)
or:
import mxnet as mx
from cnocr import CnOcr
ocr = CnOcr()
img_fp = 'examples/multi-line_cn1.png'
img = mx.image.imread(img_fp, 1)
res = ocr.ocr(img)
print("Predicted Chars:", res)
The previous codes can recognize texts in the image file examples/multi-line_cn1.png:
The OCR results shoule be:
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', '―', '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', '。']]
CnOcr.ocr_for_single_line(img_fp)
If you know that the image you're predicting contains only one line of text, function CnOcr.ocr_for_single_line(img_fp)
can be used instead。Compared with CnOcr.ocr()
, the result of CnOcr.ocr_for_single_line()
is more reliable because the process of splitting lines is not required.
Function Description
img_fp
: image file path; or color image mx.nd.NDArray
or np.ndarray
, with shape [height, width]
or [height, width, channel]
. The optional channel should be 1
(gray image) or 3
(color image).List(Char)
, such as: ['你', '好']
.Use Case:
from cnocr import CnOcr
ocr = CnOcr()
res = ocr.ocr_for_single_line('examples/rand_cn1.png')
print("Predicted Chars:", res)
or:
import mxnet as mx
from cnocr import CnOcr
ocr = CnOcr()
img_fp = 'examples/rand_cn1.png'
img = mx.image.imread(img_fp, 1)
res = ocr.ocr_for_single_line(img)
print("Predicted Chars:", res)
The previous codes can recognize texts in the image file examples/rand_cn1.png:
The OCR results shoule be:
Predicted Chars: ['笠', '淡', '嘿', '骅', '谧', '鼎', '臭', '姚', '歼', '蠢', '驼', '耳', '裔', '挝', '涯', '狗', '蒽', '子', '犷']
CnOcr.ocr_for_single_lines(img_list)
Function CnOcr.ocr_for_single_lines(img_list)
can predict a number of single-line-text image arrays batchly. Actually CnOcr.ocr(img_fp)
and CnOcr.ocr_for_single_line(img_fp)
both invoke CnOcr.ocr_for_single_lines(img_list)
internally.
Function Description
img_list
: list of images, in which each element should be a line image array, with type mx.nd.NDArray
or np.ndarray
. Each element should be a tensor with values ranging from 0
to 255
, and with shape [height, width]
or [height, width, channel]
. The optional channel should be 1
(gray image) or 3
(color image).List(List(Char))
, such as: [['第', '一', '行'], ['第', '二', '行'], ['第', '三', '行']]
.Use Case:
import mxnet as mx
from cnocr import CnOcr
ocr = CnOcr()
img_fp = 'examples/multi-line_cn1.png'
img = mx.image.imread(img_fp, 1).asnumpy()
line_imgs = line_split(img, blank=True)
line_img_list = [line_img for line_img, _ in line_imgs]
res = ocr.ocr_for_single_lines(line_img_list)
print("Predicted Chars:", res)
More use cases can be found at tests/test_cnocr.py.
python scripts/cnocr_predict.py --file examples/multi-line_cn1.png
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
Fine-tuning the model with specific data from existing models is also supported. Please refer to the following command:
python scripts/cnocr_train.py --cpu 2 --num_proc 4 --loss ctc --dataset cn_ocr --load_epoch 20
More references can be found at scripts/run_cnocr_train.sh.
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