├── 1.png
├── 2.png
├── README.md
└── main.py


/1.png:
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https://raw.githubusercontent.com/ahmedgulabkhan/TEI2S/2f7e3ea134ab8336b5d65121b5e2744f64573cf2/1.png


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/2.png:
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https://raw.githubusercontent.com/ahmedgulabkhan/TEI2S/2f7e3ea134ab8336b5d65121b5e2744f64573cf2/2.png


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/README.md:
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 1 | # TEI2S
 2 | 
 3 | ## About
 4 | TEI2S (Text Embedded Image to Speech conversion) is a project which is really helpful for the visually impaired, in a sense that it takes an image containing text embedding as the input, extracts the text from the image, and converts this text to speech, i.e; the output is an audio file containing the text which is embedded in the provided input image.
 5 | 
 6 | ## Configuration Steps
 7 | 1. Cloning the repository
 8 | 
 9 | ```
10 | $ git clone https://github.com/ahmedgulabkhan/TEI2S.git
11 | ```
12 | 
13 | 2. Installing the dependencies
14 | 
15 | First goto the [tesseract-OCR](https://github.com/tesseract-ocr/tesseract/wiki) engine, read the steps and install it on your system. After installing it, follow the steps mentioned below.
16 | 
17 | ```
18 | $ pip install numpy
19 | 
20 | $ pip install PIL
21 | 
22 | $ pip install opencv-python
23 | 
24 | $ pip install pytesseract
25 | 
26 | $ pip install gTTS
27 | ```
28 | This installs all the required dependencies like Numpy, PIL (Python Imaging Library), OpenCV, Pytesseract and gTTS.
29 | 
30 | 3. Running the file
31 | 
32 | Open the terminal and `cd` to the directory where you have cloned the repository. Then run the file by typing `python main.py`.
33 | 


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/main.py:
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 1 | import cv2
 2 | import numpy as np
 3 | import pytesseract
 4 | from PIL import Image
 5 | from gtts import gTTS
 6 | import os
 7 | 
 8 | pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
 9 | 
10 | # Path of working folder on Disk
11 | src_path = './'
12 | 
13 | def get_string(img_path):
14 |     # Read image with opencv
15 |     img = cv2.imread(img_path)
16 | 
17 |     # Convert to gray
18 |     img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
19 | 
20 |     # Apply dilation and erosion to remove some noise
21 |     kernel = np.ones((1, 1), np.uint8)
22 |     img = cv2.dilate(img, kernel, iterations=1)
23 |     img = cv2.erode(img, kernel, iterations=1)
24 | 
25 |     # Write image after removed noise
26 |     cv2.imwrite(src_path + "removed_noise.png", img)
27 | 
28 |     #  Apply threshold to get image with only black and white
29 |     img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)
30 | 
31 |     # Write the image after apply opencv to do some ...
32 |     cv2.imwrite(src_path + "thres.png", img)
33 | 
34 |     # Recognize text with tesseract for python
35 |     result = pytesseract.image_to_string(Image.open(src_path + "thres.png"))
36 | 
37 |     # Remove template file
38 |     #os.remove(temp)
39 | 
40 |     return result
41 | 
42 | 
43 | print('--- Recognizing text from image ---')
44 | img2txt = get_string(src_path + "2.png")
45 | print(img2txt)
46 | 
47 | myobj = gTTS(text=img2txt, lang='en', slow=False)
48 | myobj.save('output1.mp3')
49 | #os.system('mpg321 output1.mp3')
50 | os.system('output1.mp3')
51 | print("------ Done -------")
52 | 


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