Unless you provide clear images (black letters on white background) (maybe
with straight text, but this could be handle by leptonica) you can not
expect that tesseract will provide you correct results.


Zdenko


ut 22. 10. 2019 o 10:00 Sangharsh Kamble <[email protected]>
napĂ­sal(a):

> [image: 2.jpeg]
>
> [image: 4.jpeg]
>
> [image: ALARM_ANPR_2019_09_26_00_09_48_0878_crop.jpg]
>
> [image: ALARM_ANPR_2019_09_26_00_48_02_0976_crop.jpg]
>
> import cv2
> import numpy as np
> from PIL import Image
> import pytesseract
> from scipy import ndimage
> from scipy.ndimage import rotate
> #from matplotlib import pyplot as plt
>
> import allow_needed_values as anv
>
> img = cv2.imread('path_to_image/1.jpg')
> result = cv2.fastNlMeansDenoisingColored(img,None,10,10,7,21)
> cv2.imshow("denoise",result)
> gray = cv2.cvtColor(result, cv2.COLOR_RGB2GRAY)
> cv2.imshow("gray image",gray)
>
> gray = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
> cv2.imshow("threshold image", gray)
> #cv2.waitKey(delay = 0)
> #gray = cv2.medianBlur(gray, 3)
> img = cv2.medianBlur(gray,3)
> cv2.imshow("median blur", img)
> #rot = rotate(img, -6, reshape=False)
> #rotated= ndimage.rotate(img,-2)
> #cv2.imshow("rotate image", rot)
> #result = cv2.fastNlMeansDenoisingColored(img,None,20,10,7,21)
> rot = rotate(img, -5, reshape=False)
> cv2.imshow("rotate", rot)
> tessdata_dir_config = "/usr/share/tesseract-ocr/4.00/tessdata/"
> text=pytesseract.image_to_string(rot,lang='eng',config=tessdata_dir_config)
> carReg = anv.catch_rectify_plate_characters(text)
> print(carReg)
> cv2.waitKey(delay = 0)
>
>
> Here i write the code for number recognition from number plate images,but
> the tessaract failed to recognize number. I have day light and night
> images. in most of the case ocr failed on night images. also i apply
> various filter filter for images clearing and also rotate the image angle
> for some degree.
> I upload the various sample images where tesseract ocr failed.
>
>
>
>
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