How to Create an Image to Text Converter Python

# Convert to grayscalegray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply thresholding_, threshold_image = cv2.threshold(gray_image, 150, 255, cv2.THRESH_BINARY)
# Resize the image (optional, adjust size as needed)resized_image = cv2.resize(threshold_image, (800, 600))

Note: We have used the image dimension 800×600. You can adjust them as per your needs. 

Now comes the most important part i.e., extracting text from images. For this, you have to use the Pytesseract library. First, you’ll need to feed the image to Tesseract and then get the text. 

Below is the code that you are going to need for text extraction. 

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# Extract text from the imageextracted_text = pytesseract.image_to_string(resized_image)

This line uses pytesseract.image_to_string() to extract the text from the image and store it in the extracted_text variable. 

Easy, right?

5. Displaying and Saving Extracted Text

Once you have the text extracted the next step is to display it on the screen. You can also save it in a .txt file. 

To display the extracted text run the code below.

print(extracted_text)

This will print the extracted text in your console.

To save the text to a file run this code.

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with open('extracted_text.txt', 'w') as file:    file.write(extracted_text)

This will create a new file called extracted_text.txt and save all the extracted text inside it.

You’ve successfully created your own image-to-text converter. Now all you need to do is to change the image path, run the same commands, and start extracting the text. 

Enhancing the Converter

Now that you have built a simple image-to-text converter. Let’s enhance it further. Below we’ll walk you through a few ways that you can opt for enhancing your tool. 

Adding GUI Support

Working with a command line tool is a bit technical. Having a graphical user interface (GUI) can make the process easier. For example, look at the image below. 

It is the interface of Image to Text Converter. As you can see it is easier for a user to interact with the tool. They can extract text by simply clicking buttons. There is no need to type commands. 

Libraries like Tkinter and PyQt5 can help you create GUI. Here is a simple example of using Tkinter to create a basic GUI for uploading an image and displaying the extracted text:

First, you need to install Tkinter (if it is not already installed):

pip install tk

After installing Tkinter run the below code for GUI.

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import tkinter as tkfrom tkinter import filedialogfrom PIL import ImageTk, Imageimport pytesseractimport cv2
# Create the main windowroot = tk.Tk()root.title("Image-to-Text Converter")
# Function to browse and load an imagedef upload_image():    file_path = filedialog.askopenfilename(title="Select an Image", filetypes=[("Image files", "*.jpg;*.jpeg;*.png")])    if file_path:        img = cv2.imread(file_path)        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert to grayscale        text = pytesseract.image_to_string(img)  # Extract text
        # Display the extracted text in a text box        text_box.delete(1.0, tk.END)        text_box.insert(tk.END, text)
# Create buttons and text area for GUIupload_btn = tk.Button(root, text="Upload Image", command=upload_image)upload_btn.pack(pady=10)
text_box = tk.Text(root, height=10, width=50)text_box.pack(pady=20)
# Run the Tkinter event looproot.mainloop()

Things you should know about the above code.

• Created a simple window (root) with a button to upload images. The uploaded image is processed, and the extracted text is displayed in a text box.
• The filedialog.askopenfilename() will let the user select an image file from their system.
• After processing, the extracted text will appear in the text box.

Batch Processing

You can also make your tool to process multiple images in one go. For this, you have to modify your script so that it can handle batch processing. 

For this, you have to run the code we have shared below. 

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import os
# Function to process all images in a folderdef process_images_in_folder(folder_path):    for filename in os.listdir(folder_path):        if filename.endswith(('.jpg', '.jpeg', '.png')):            image_path = os.path.join(folder_path, filename)            img = cv2.imread(image_path)            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)            text = pytesseract.image_to_string(img)
            # Save the extracted text to a file            with open(f"{filename}_extracted.txt", 'w') as file:                file.write(text)
# Specify folder pathfolder_path="path/to/your/folder"
# Call the function to process images in the folderprocess_images_in_folder(folder_path)

The above code will make your script go through each image file. It will process each image, extract the text using Tesseract, and further save the text as a separate .txt file. 

Key Takeaways

In the above blog post, we have shared the complete process of building an image-to-text converter using Python. Try implementing them and start creating your own image-to-text conversion tool. Turn to experiment, learn, and create something extraordinary.