liteocr 0.2.1

>> liteocr

对图像文件执行ocr并将识别结果写入 json。

usage: LiteOCR [-h] [-d] [--extra-whitelist str] [--all-unicode] [--lang str]
               [--min-text-size int] [--max-text-size int]
               [--uniformity-thresh :0.0<=float<1.0]
               [--thin-line-thresh :odd int] [--conf-thresh :0<=int<100]
               [--box-expand-factor :0.0<=float<1.0]
               [--horizontal-pooling int]
               str str

positional arguments:
  str                   image file
  str                   output JSON file

optional arguments:
  -h, --help            show this help message and exit
  -d, --display         display recognized bounding boxes and text on top of the image

engine:
  parameters to liteocr.OCREngine constructor

  --extra-whitelist str
                        string of extra chars for Tesseract to consider only
                        takes effect when all_unicode is False
  --all-unicode         if True, Tesseract will consider all possible unicode
                        characters
  --lang str            language in the text. Defaults to English.

recognition:
  parameters to OCREngine.recognize() method

  --min-text-size int   min text height/width in pixels, below which will be
                        ignored
  --max-text-size int   max text height/width in pixels, above which will be
                        ignored
  --uniformity-thresh :0.0<=float<1.0
                        ignore a region if the number of pixels neither black
                        nor white < [thresh]
  --thin-line-thresh :odd int
                        remove all lines thinner than [thresh] pixels.can be
                        used to remove the thin borders of web page textboxes.
  --conf-thresh :0<=int<100
                        ignore regions with OCR confidence < thresh.
  --box-expand-factor :0.0<=float<1.0
                        expand the bounding box outwards in case certain chars
                        are cutoff.
  --horizontal-pooling int
                        result bounding boxes will be more connected with more
                        pooling, but large pooling might lower accuracy.

python3库

fromliteocrimportOCREngine,load_img,draw_rect,draw_text,dispimage_file='my_img.png'img=load_img(image_file)# you can either use context manager or call engine.close() manually at the end.withOCREngine()asengine:# engine.recognize() can accept a file name, a numpy image, or a PIL image.fortext,box,confinengine.recognize(image_file):print(box,'\tconfidence =',conf,'\ttext =',text)draw_rect(img,box)draw_text(img,text,box,color='bw')# display the image with recognized text boxes overlaiddisp(img,pause=False)