image-table-ocr/table_ocr/ocr_image.py

import argparse
import math
import os
import sys

import cv2
import pytesseract

description="""Takes a single argument that is the image to OCR.
Remaining arguments are passed directly to Tesseract.

Attempts to make OCR more accurate by performing some modifications on the image.
Saves the modified image and the OCR text in an `ocr_data` directory.
Filenames are of the format for training with tesstrain."""
parser = argparse.ArgumentParser(description=description)
parser.add_argument("image", help="filepath of image to perform OCR")

def crop_to_text(image):
    MAX_COLOR_VAL = 255
    BLOCK_SIZE = 15
    SUBTRACT_FROM_MEAN = -2

    img_bin = cv2.adaptiveThreshold(
        ~image,
        MAX_COLOR_VAL,
        cv2.ADAPTIVE_THRESH_MEAN_C,
        cv2.THRESH_BINARY,
        BLOCK_SIZE,
        SUBTRACT_FROM_MEAN,
    )

    img_h, img_w = image.shape
    horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (int(img_w * 0.5), 1))
    vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, int(img_h * 0.7)))
    horizontal_lines = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
    vertical_lines = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
    both = horizontal_lines + vertical_lines
    cleaned = img_bin - both

    # Get rid of little noise.
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
    opened = cv2.morphologyEx(cleaned, cv2.MORPH_OPEN, kernel)

    contours, hierarchy = cv2.findContours(opened, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    bounding_rects = [cv2.boundingRect(c) for c in contours]
    NUM_PX_COMMA = 6
    if bounding_rects:
        minx, miny, maxx, maxy = math.inf, math.inf, 0, 0
        for x, y, w, h in bounding_rects:
            minx = min(minx, x)
            miny = min(miny, y)
            maxx = max(maxx, x + w)
            maxy = max(maxy, y + h)
        x, y, w, h = minx, miny, maxx - minx, maxy - miny
        cropped = image[y:min(img_h, y+h+NUM_PX_COMMA), x:min(img_w, x+w)]
    else:
        # If we morphed out all of the text, fallback to using the unmorphed image.
        cropped = image
    bordered = cv2.copyMakeBorder(cropped, 5, 5, 5, 5, cv2.BORDER_CONSTANT, None, 255)
    return bordered
def ocr_image(image, config):
    return pytesseract.image_to_string(
        image,
        config=config
    )

def main(image_file, tess_args):
    directory, filename = os.path.split(image_file)
    filename_sans_ext, ext = os.path.splitext(filename)
    image = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE)
    cropped = crop_to_text(image)
    ocr_data_dir = os.path.join(directory, "ocr_data")
    os.makedirs(ocr_data_dir, exist_ok=True)
    out_imagepath = os.path.join(ocr_data_dir, filename)
    out_txtpath = os.path.join(ocr_data_dir, "{}.gt.txt".format(filename_sans_ext))
    cv2.imwrite(out_imagepath, cropped)
    txt = ocr_image(cropped, " ".join(tess_args))
    print(txt)
    with open(out_txtpath, "w") as txt_file:
        txt_file.write(txt)

if __name__ == "__main__":
    args, tess_args = parser.parse_known_args()
    main(args.image, tess_args)