[FIXED] PyTesseract unable to recognize text on a green background

Issue

I have been playing around with the image while preprocessing but tesseract is unable to detect the text on the LCD screen. It does create a bounding box around it which, I guess, means it found something in there but does not give any text as output.

Image I am working with:

Here is my code:

import cv2
import pytesseract
import numpy as np

img = cv2.imread("test-python2.jpg")

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ret, thresh1 = cv2.threshold(gray, 50, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)

rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (18, 18))
kernel = np.ones((5, 5), np.uint8)
#closin = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel)

dilation = cv2.dilate(thresh1, rect_kernel, iterations = 1)

contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
im2 = img.copy()
for cnt in contours:
    x, y, w, h = cv2.boundingRect(cnt)
    im2 = cv2.rectangle(im2, (x, y), (x + w, y + h), (0, 255, 0), 2)
    
    cropped = img[y:y + h, x:x + w]
    text = pytesseract.image_to_string(cropped)
    im2 = cv2.putText(im2, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 3)
    text2 = text.encode('latin-1', 'replace').decode('latin-1')
    print (text2)


cv2.imshow("", im2)
cv2.waitKey(0)

And the output I get from the cv2.imshow() is here:

The text I did get is accurate enough but it just does not read off of the LCD screen. I have tried playing around with different kinds of binarization/thresholding but the LCD never seems to get picked up. The recognization of the LCD is very important to my project but I have been stuck at this hurdle for a while now.

Solution

I cut out the interessting part:

import subprocess
import cv2
import pytesseract

# Image manipulation
# Commands https://imagemagick.org/script/convert.php
mag_img = r'D:\Programme\ImageMagic\magick.exe'
con_bw = r"D:\Programme\ImageMagic\convert.exe"

in_file = r'green_red.jpg'
out_file = r'green_bw.jpg'

# Play with black and white and contrast for better results
process = subprocess.run([con_bw, in_file, "-resize", "100%","-threshold","28%", out_file])

# Text ptocessing
pytesseract.pytesseract.tesseract_cmd=r'C:\Program Files\Tesseract-OCR\tesseract.exe'
img = cv2.imread(out_file)

# Parameters see tesseract doc 
custom_config = r'--psm 6 --oem 3 -c tessedit_char_whitelist=ABCDEFTGHIJKLMNOPQRSTUVWXYZkh' 

tex = pytesseract.image_to_string(img, config=custom_config)
print(tex)

with open("number.txt", 'w') as f:
    f.writelines(tex)

cv2.imshow('image',img)
cv2.waitKey(12000)
cv2.destroyAllWindows()

Output:

CTEP

Image:

Option 2 with CV2 instead of imagemagick. I cropped the interessting area and set it to blach&white, before ocr the text:

import cv2
import numpy as np
import pytesseract
 
img = cv2.imread('green.jpg',cv2.IMREAD_UNCHANGED)
print(img.shape) # Print image shape
cv2.imshow("Original", img)
 
# Cropping an image and set color to B&W
cropped_image = img[370:600, 603:1360] # img[y:y+h, x:x+w]
grayImage = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2GRAY)
(thresh, blackAndWhiteImage) = cv2.threshold(grayImage, 65, 255, cv2.THRESH_BINARY) # | cv2.THRESH_OTSU => 90
print(thresh)

# resize image
scale_percent = 43 # percent of original size
width = int(blackAndWhiteImage.shape[1] * scale_percent / 100)
height = int(blackAndWhiteImage.shape[0] * scale_percent / 100)
dim = (width, height)
resized = cv2.resize(blackAndWhiteImage, dim, interpolation = cv2.INTER_AREA)

# OCR resized Black & White image
pytesseract.pytesseract.tesseract_cmd=r'C:\Program Files\Tesseract-OCR\tesseract.exe'
custom_config = r'--psm 6 --oem 3 -c tessedit_char_whitelist=ABCDEFTGHIJKLMNOPQRSTUVWXYZkh' 
tex = pytesseract.image_to_string(resized, config=custom_config)
print(tex)

# Display cropped image
cv2.imshow("cropped", resized)
 
# Save the cropped image
cv2.imwrite("Cropped Image.jpg", resized)
 
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

(1080, 1920, 3)  # image size
65.0             # thresh value
CTEP             # -> OCR

Answered By - Hermann12