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import cv2
import pytesseract

# Mention the installed location of Tesseract-OCR in system
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'

# Read image from which text needs to be extracted
img = cv2.imread(r"C:\Users\user\Temp\1.jpg")

# 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, (10, 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, lang='rus')
    if len(text) > 1 or str(text) != any(('\n', '', ' ')):
        # Appending the text into file
        file.write(text)
        # file.write("\n")

    # Close the file
    file.close()