Programming for Everyone

Posted: **Sun Nov 24, 2024 8:02 am**

We will implement a sophisticated utility that will be able to remove text from images while enhancing the quality of those images. The solution makes use of advanced AI techniques: **Denoising Autoencoders**, which reduce noise, and **Super-Resolution Convolutional Networks (SRCNN)**, which increase image sharpness. The utility will remove text from images without leaving noticeable side effects or distorting the images while improving the image resolution and sharpness.

**Key Objectives:**

1. **Text Removal with Autoencoders:**
- The idea is to apply Denoising Autoencoders in order to remove unwanted text from any kind of image without loss of the original content.
- The process shall not affect the quality/structure of the underlying picture.

2. **Image Enhancement Using SRCNN:**
- Super-Resolution Convolutional Networks will be used to enhance the resolution of the already processed image so that its sharpness increases.
- Image sharpness optimization: make sure that sharpened images retain natural and high-quality visuals.

3. **OCR Integration Optional:**
- Integrate Optical Character Recognition (OCR) using Tesseract for the detection and processing of remaining text for further refinement of the model's accuracy.

4. **Testing & Optimization:**
- Test the tool on various images, including those with complex text patterns or backgrounds.
Optimize the performance for large images with no great losses either in processing speed or in image quality.

**Technical Requirements:**
Experience in Deep Learning models, specifically Autoencoder and Super-Resolution networks.
Exposure to Image Processing libraries: OpenCV and TensorFlow/Keras
Knowledge of OCR using Tesseract to extract text from images
Python, model optimization techniques to realize efficiency in performance

Expected Deliverables:
Full working tool that does text removal and image enhancement using AI techniques. Detailed documentation about development steps: setup and usage instructions. Test results with a series of images where the tool was used to prove its effectiveness.

Posted: **Sun Nov 24, 2024 8:07 am**

Below is the full Python code with all the steps for **Text Removal** using **Autoencoders** and **Super-Resolution Convolutional Networks (SRCNN)**. This code will include necessary components like noise reduction, text removal, and image enhancement. I'll also integrate **OpenCV**, **Denoising Autoencoders**, and **SRCNN**.

Make sure you have the following dependencies installed:

```bash
pip install opencv-python opencv-python-headless numpy tensorflow keras pytesseract scikit-image
```

### Full Python Code for **Clean Text Removal with Autoencoders and SRCNN**

```python

Code: Select all

import cv2
import numpy as np
import tensorflow as tf
from keras.models import load_model
from skimage.restoration import denoise_tv_chambolle
import pytesseract
from skimage import img_as_ubyte

# Function to load the pre-trained Autoencoder model for noise reduction
def load_autoencoder_model():
    # Assuming you have a trained autoencoder saved as 'autoencoder_model.h5'
    model = load_model('autoencoder_model.h5')  # Replace with your model's path
    return model

# Function to load the SRCNN model for image super-resolution enhancement
def load_srcnn_model():
    # Assuming you have a trained SRCNN model saved as 'srcnn_model.h5'
    model = load_model('srcnn_model.h5')  # Replace with your model's path
    return model

# Function to remove text using inpainting (OpenCV)
def remove_text_using_inpainting(image):
    # Convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Thresholding to find the text regions (may need tuning)
    _, thresh = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY_INV)
    
    # Find contours and fill the text regions with white (or use inpainting)
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    for contour in contours:
        cv2.drawContours(image, [contour], -1, (255, 255, 255), -1)  # Fill the contour with white
    
    # Inpainting to fill the gaps left after removing text
    result = cv2.inpaint(image, thresh, 3, cv2.INPAINT_TELEA)
    return result

# Function to perform denoising using Denoising Autoencoder
def denoise_image_with_autoencoder(image, autoencoder_model):
    # Normalize the image to [0, 1] range before feeding into the autoencoder
    image_normalized = image / 255.0
    image_normalized = np.expand_dims(image_normalized, axis=0)  # Adding batch dimension
    
    # Predict denoised image using the autoencoder
    denoised_image = autoencoder_model.predict(image_normalized)
    
    # Convert back to the original image scale
    denoised_image = np.squeeze(denoised_image, axis=0)  # Remove batch dimension
    denoised_image = (denoised_image * 255).astype(np.uint8)
    return denoised_image

# Function to perform super-resolution using SRCNN
def enhance_image_with_srcnn(image, srcnn_model):
    # Normalize the image to [0, 1]
    image_normalized = image / 255.0
    image_normalized = np.expand_dims(image_normalized, axis=0)  # Add batch dimension
    
    # Predict enhanced image using SRCNN
    enhanced_image = srcnn_model.predict(image_normalized)
    
    # Convert back to original scale
    enhanced_image = np.squeeze(enhanced_image, axis=0)
    enhanced_image = (enhanced_image * 255).astype(np.uint8)
    return enhanced_image

# Function to extract text from an image using OCR (Tesseract)
def extract_text_from_image(image):
    text = pytesseract.image_to_string(image)
    return text

# Main function to process the image
def process_image(image_path):
    # Load image
    image = cv2.imread(image_path)

    # Load pre-trained models (Autoencoder and SRCNN)
    autoencoder_model = load_autoencoder_model()
    srcnn_model = load_srcnn_model()

    # Step 1: Remove text from image using inpainting
    image_no_text = remove_text_using_inpainting(image.copy())

    # Step 2: Denoise the image using Denoising Autoencoder
    denoised_image = denoise_image_with_autoencoder(image_no_text, autoencoder_model)

    # Step 3: Enhance the image using SRCNN (Super-Resolution)
    enhanced_image = enhance_image_with_srcnn(denoised_image, srcnn_model)

    # Step 4: Extract text from the enhanced image
    extracted_text = extract_text_from_image(enhanced_image)

    # Display the results
    print("Extracted Text: ", extracted_text)

    # Show images
    cv2.imshow("Original Image", image)
    cv2.imshow("Text Removed Image", image_no_text)
    cv2.imshow("Denoised Image", denoised_image)
    cv2.imshow("Enhanced Image", enhanced_image)
    
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Example usage
image_path = 'path_to_your_image.jpg'  # Replace with your image path
process_image(image_path)

```

### Code Breakdown:

1. **Text Removal**:
- Using OpenCV’s `cv2.inpaint` method to fill in the areas of text with surrounding pixels.
- The image is thresholded and contours are found to help locate the text.

2. **Denoising with Autoencoder**:
- A pre-trained Autoencoder model (which should be trained separately) is used to reduce noise in the image.
- The model takes the input image, processes it, and returns a denoised version.

3. **Image Enhancement with SRCNN**:
- A pre-trained SRCNN model (Super-Resolution Convolutional Neural Network) is used to enhance the resolution and clarity of the image.
- The SRCNN model predicts a high-resolution version of the low-resolution input image.

4. **Text Extraction**:
- Tesseract OCR is used to extract any remaining text from the processed image.

### Assumptions:
- The Autoencoder and SRCNN models must be pre-trained and saved as `autoencoder_model.h5` and `srcnn_model.h5` respectively. These models are expected to handle denoising and image resolution enhancement.
- You can use any dataset to train these models (Autoencoders for denoising and SRCNN for super-resolution).

### Dependencies:
- **OpenCV** for image processing (thresholding, inpainting).
- **TensorFlow/Keras** for loading pre-trained Autoencoder and SRCNN models.
- **Tesseract OCR** for text extraction.
- **Scikit-image** for denoising (optional, for noise reduction if not using autoencoders).

### How to Use:
1. Prepare your pre-trained models for Autoencoders and SRCNN.
2. Provide the path to an image you want to process (with text).
3. The program will display the original, text-removed, denoised, and enhanced images and print any extracted text.

Programming for Everyone

Clean Text Removal with Autoencoders and SRCNN

Clean Text Removal with Autoencoders and SRCNN

Re: Clean Text Removal with Autoencoders and SRCNN