# Import required packages
import cv2
import pytesseract
# Mention the installed location of Tesseract-OCR in your system
pytesseract.pytesseract.tesseract_cmd = '/bin/tesseract’ # In case using colab after installing above modules
# Read image from which text needs to be extracted
img = cv2.imread(„sample.jpg”)
# Preprocessing the image starts
# Convert the image to gray scale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Performing OTSU threshold
ret, thresh1 = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)
# Specify structure shape and kernel size.
# Kernel size increases or decreases the area
# of the rectangle to be detected.
# A smaller value like (10, 10) will detect
# each word instead of a sentence.
rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (18, 18))
# Applying dilation on the threshold image
dilation = cv2.dilate(thresh1, rect_kernel, iterations = 1)
# Finding contours
contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
# Creating a copy of image
im2 = img.copy()
# A text file is created and flushed
file = open(„recognized.txt”, „w+”)
file.write(„”)
file.close()
# Looping through the identified contours
# Then rectangular part is cropped and passed on
# to pytesseract for extracting text from it
# Extracted text is then written into the text file
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
# Drawing a rectangle on copied image
rect = cv2.rectangle(im2, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Cropping the text block for giving input to OCR
cropped = im2[y:y + h, x:x + w]
# Open the file in append mode
file = open(„recognized.txt”, „a”)
# Apply OCR on the cropped image
text = pytesseract.image_to_string(cropped)
# Appending the text into file
file.write(text)
file.write(„\n„)
# Close the file
file.close()
# This code is modified by Susobhan Akhuli