Image to text recognition using Tesseract-OCR is better when Image is preprocessed manually using Gimp than my Python Code

Question

I am trying to write code in Python for the manual Image preprocessing and recognition using Tesseract-OCR.

Manual process:
For manually recognizing text for a single Image, I preprocess the Image using Gimp and create a TIF image. Then I feed it to Tesseract-OCR which recognizes it correctly.

To preprocess the image using Gimp I do -

1. Change mode to RGB / Grayscale
   Menu -- Image -- Mode -- RGB
2. Thresholding
   Menu -- Tools -- Color Tools -- Threshold -- Auto
3. Change mode to Indexed
   Menu -- Image -- Mode -- Indexed
4. Resize / Scale to Width > 300px
   Menu -- Image -- Scale image -- Width=300
5. Save as Tif

Then I feed it tesseract -

$ tesseract captcha.tif output -psm 6

And I get an accurate result all the time.

Python Code:
I have tried to replicate above procedure using OpenCV and Tesseract -

def binarize_image_using_opencv(captcha_path, binary_image_path='input-black-n-white.jpg'):
    im_gray = cv2.imread(captcha_path, cv2.CV_LOAD_IMAGE_GRAYSCALE)
    (thresh, im_bw) = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    # although thresh is used below, gonna pick something suitable
    im_bw = cv2.threshold(im_gray, thresh, 255, cv2.THRESH_BINARY)[1]
    cv2.imwrite(binary_image_path, im_bw)

    return binary_image_path

def preprocess_image_using_opencv(captcha_path):
    bin_image_path = binarize_image_using_opencv(captcha_path)

    im_bin = Image.open(bin_image_path)
    basewidth = 300  # in pixels
    wpercent = (basewidth/float(im_bin.size[0]))
    hsize = int((float(im_bin.size[1])*float(wpercent)))
    big = im_bin.resize((basewidth, hsize), Image.NEAREST)

    # tesseract-ocr only works with TIF so save the bigger image in that format
    tif_file = "input-NEAREST.tif"
    big.save(tif_file)

    return tif_file

def get_captcha_text_from_captcha_image(captcha_path):

    # Preprocess the image befor OCR
    tif_file = preprocess_image_using_opencv(captcha_path)

    #   Perform OCR using tesseract-ocr library
    # OCR : Optical Character Recognition
    image = Image.open(tif_file)
    ocr_text = image_to_string(image, config="-psm 6")
    alphanumeric_text = ''.join(e for e in ocr_text)

    return alphanumeric_text    

But I am not getting the same accuracy. What did I miss?

Update 1:

1. Original Image
   
2. Tif Image created using Gimp
   
3. Tif Image created by my python code
   

Update 2:

This code is available at https://github.com/hussaintamboli/python-image-to-text

Answer 1

如果输出与您的预期输出（即额外的 '," 等，如您的评论中所建议的那样）只是最小偏差，请尝试将字符识别限制为您期望的字符集（例如字母数字）。

Answer 2

You have already applied the simple thresholding. The missing part is you need to read the images one-by-one

For each single-digit

• 1. Upsample
• 2. Add border

Upsampling is required for accurate recognition. Adding border to the image will center the digit.

8	8	B	C	7	F

Code:

---

import cv2
import pytesseract

img = cv2.imread('Iv5BS.jpg')
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.threshold(gry, 128, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

(h_thr, w_thr) = thr.shape[:2]
s_idx = 2
e_idx = int(w_thr/6) - 20
result = ""

for _ in range(0, 6):
    crp = thr[5:int((6*h_thr)/7), s_idx:e_idx]
    (h_crp, w_crp) = crp.shape[:2]
    crp = cv2.resize(crp, (w_crp*2, h_crp*2))
    crp = cv2.copyMakeBorder(crp, 10, 10, 10, 10, cv2.BORDER_CONSTANT, value=255)
    s_idx = e_idx
    e_idx = s_idx + int(w_thr/6) - 7
    txt = pytesseract.image_to_string(crp, config="--psm 6")
    result += txt[0]
    cv2.imshow("crp", crp)
    cv2.waitKey(0)

print(result)

Result :

88BC7F