>> liteocr

Performs OCR on an image file and writes the recognition results to 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 library

from liteocr import OCREngine, load_img, draw_rect, draw_text, disp

image_file = 'my_img.png'
img = load_img(image_file)

# you can either use context manager or call engine.close() manually at the end.
with OCREngine() as engine:
    # engine.recognize() can accept a file name, a numpy image, or a PIL image.
    for text, box, conf in engine.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 overlaid
disp(img, pause=False)