CODEWITHSUNDEEP
pythonimageopencvtesseractpython-tesseract
Hussain
Hussain

Reputation: 5177

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

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
    enter image description here
  2. Tif Image created using Gimp
    enter image description here
  3. Tif Image created by my python code
    enter image description here

Update 2:

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

Upvotes: 7

Views: 10987

Answers (2)

Ahx
Ahx

Reputation: 7985

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
    1. Add border

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

Upvotes: 2

Aurelian
Aurelian

Reputation: 11

If the output is only minimally deviating from your expected output (i.e. extra '," etc. as suggested in your comments) try limiting character recognition to the character set you expect (e.g. alphanumeric).

Upvotes: 1

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