Report 2:

Now that officially first evaluation is finished and not to mentioned but I passed, here is my 2nd report for Image Processing (Image to text).

Project- Poor man’s rekognition

Proposal

Github

Mentor- Johannes Lochter

Organization- CCExtractor Development

Org admin- Carlos Fernandez

May 27 — June 1 = complete use-cases 1,2 and 3 (completed)

June 3 — June 15 = complete use-cases 4 (completed)

June 17 — June 19 =1st report (completed)

June 20 — July 3=complete use-case 5(completed)

July 4 — July 6=2nd report(completed)

Well, first evaluation is officially over and I have completed my part with all of my dedication and full determination. So in this article, I will only be discussing my last use-case and that is extraction of text from an image file.

Use-case 5

Extraction of text from an image.

Code 1.

Extraction of text from an image is a subpart of image processing and is called OPTICAL CHARACTER RECOGNITION (OCR). I have used Tesseract which is an OCR engine developed by Google. It supports Unicode and has the ability to recognize more than 100 languages. I personally think it is super cool ✌.

Requirements:

1. Pytesseract
2. Pillow

I have added multiple libraries to experiment but one can only import this 2 libraries and everything will be smooth.

4.png

Let’s jump to the coding part

#import libraries
import cv2
import numpy as np
import pytesseract
from PIL import Image
img = Image.open('4.png')
pytesseract.pytesseract.tesseract_cmd = 'C:/Users/Faiz Khan/Desktop/ddd/text recognition/Tesseract-OCR/tesseract'
result = pytesseract.image_to_string(img)
with open('reader.txt', mode='w') as file:
    file.write(result)

reader.txt

Here I have basically read the texts from an image and have made a .txt file of the result.

Code 2.

This is a second which was my main code but I was unable to properly process it because of some error but have thought to complete it as soon as I get time. Here I did perform filters in order to get a clear image.

Requirements:

1. OpenCV
2. Numpy
3. Tesseract
4. Pillow

Let’s jump to the coding part.

import cv2
import numpy as np
from PIL import Image
from pytesseract import image_to_string
# Path of working folder on Disk
src_path = "C:/Users/Faiz Khan/Desktop/ddd/text recognition"
def get_string(img_path):
# Read image with opencv
    img = cv2.imread('C:/Users/Faiz Khan/Desktop/ddd/text recognition/2.png')

    # Convert to gray
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Apply dilation and erosion to remove some noise
    kernel = np.ones((1, 1), np.uint8)
    img = cv2.dilate(img, kernel, iterations=1)
    img = cv2.erode(img, kernel, iterations=1)

    # Write image after removed noise
    cv2.imwrite(src_path + "removed_noise.png", img)
    #  Apply threshold to get image with only black and white
    img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)

    # Write the image after apply opencv to do some ...
    cv2.imwrite(src_path + "thres.png", img)

    # Recognize text with tesseract for python
    result = pytesseract.image_to_string(Image.open(src_path + "thres.png"))

   

    return result
print(get_string(src_path + "cool.jpg") )

Much more can be done here.

Also if anyone is able to figure out the errors and solution to the above-mentioned code, please let me know.

The amazing journey so far.