Noise Reduction and Image Optimization for Captcha

This dataset is specifically designed for preprocessing captcha images, focusing on removing intentional noise and enhancing the clarity of numbers to improve automated recognition accuracy. It is sourced from the widely used Captcha Dataset on Kaggle, which contains challenging captcha images with added noise to prevent easy machine interpretation.

Objective

The primary goal of this dataset is to optimize captcha image processing by reducing visual complexity and improving readability. This preprocessing pipeline enhances machine learning models’ ability to accurately recognize numbers in captchas, making it highly beneficial for AI-driven CAPTCHA solvers and OCR-based recognition systems.

Key Features

• Noise Reduction: Advanced filtering techniques to remove unwanted artifacts.
• Edge Enhancement: Sharpening of numeric characters for better distinction.
• Contrast Adjustment: Improved visibility of numbers against the background.
• Binarization & Thresholding: Converting images into easily recognizable binary formats.
• Morphological Transformations: Refinement of character shapes for increased model accuracy.