How to make images to python converter text

# 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 800 × 600 image dimension. You can adjust it according to your needs.

Now comes the most important part that is, extracting text from the image. For this, you must use Pytessert Library. First, you must feed the picture to the tesseract and then get the text.

Below is the code that you will need to extract text.

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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 Extraction_Text variable.

Easy, right?

5. Display and save extracted text

After your text is extracted, the next step is to display it on the screen. You can also save it in the .txt file.

To display the extracted text, run the code below.

print (extraction_text)

This will print the extracted text on your console.

To save the text to the 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 text extracted in it.

You have successfully made your own image-to-text converter. Now all you need to do is change the path of the image, run the same command, and start extracting text.

Increase converter

Now you have built a simple image-to-text converter. Let’s increase further. Below we will guide you through several ways you can choose to increase your tools.

Adding GUI support

Working with a slightly technical command line. Having a graphics user interface (GUI) can make the process easier. For example, see the picture below.

This is an image interface to the text converter. As you can see, it’s easier for users to interact with this tool. They can extract the text by just clicking the button. No need to type the command.

Library likes TKINTER and PYQT5 Can help you make a GUI. Here is a simple example of use Tkinter To create a basic GUI to upload images and display extracted texts:

First, you need to install TKINTER (if not installed):

Pip Install Tk

After installing Tkinter, run the code below 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 code above.

• Make a simple window (root) with a button to upload an image. The uploaded images are processed, and the extracted text is displayed in the text box.
• Filedialog.Ascopenfilename () will let users select image files from their system.
• After being processed, the extracted text will appear in the text box.

Batch processing

You can also make your tools to process some images in one way. For this, you must modify your script so that you can handle batch processing.

For this, you must run the code that 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 code above will create your script through each image file. This will process each image, extract the text using a tesseract, and further save the text as a separate .txt file.

Takeaways key

In the blog post above, we have shared a complete process of building images-to-text converters using Python. Try to implement and start making your own-to-text image conversion tools. Switch to experimenting, learning, and creating something extraordinary.