Captcha Solving Using CNN

Welcome to my project on solving text-based captchas using a Convolutional Neural Network (CNN)! This project was built in under 48 hours, and Im excited to share the journey and insights I gained along the way. 🧠🚀

Table of Contents

1. Project Overview
2. Key Learnings
3. Technologies Used
4. How It Works
5. Getting Started
6. Inspiration
7. Contact

Project Overview

I started this project with the goal of solving text-based captchas using Deep Learning. Captchas are widely used to differentiate between human users and bots, but as AI advances, it's becoming easier to solve even the simplest captchas using Convolutional Neural Networks (CNNs). This project explores that challenge, leveraging the power of CNNs to solve these captchas.

Key Learnings

Throughout the development of this project, I learned a ton about deep learning and CNNs. Here are some of the key things I learned:

• Neural Networks: Gained a solid understanding of how neural networks work and how CNNs specialize in image-related tasks.

• Libraries: Mastered libraries like TensorFlow, OpenCV, Keras, Numpy, Matplotlib, and Pandas for image processing and deep learning.

• Preprocessing: Learned the importance of preprocessing images (resizing, normalizing, noise reduction) to make them suitable for neural networks.

• CNN Layers: Deep-dived into CNN concepts like convolutional layers, Maxpooling, and Dense layers, and understood their role in feature extraction and classification.

• The Power of AI: Realized the immense potential AI has in solving real-world problems, and this project sparked a deeper curiosity to explore more AI techniques.

Technologies Used

• TensorFlow: For building the CNN model and training it on captcha images.

• OpenCV: For image processing tasks like resizing and noise reduction.

• Keras: A high-level neural networks API for easier and faster prototyping.

• NumPy: For numerical computations and handling arrays.

• Matplotlib: For visualizing the training process and results.

How It Works

The model uses a Convolutional Neural Network (CNN) to analyze and solve captchas. The basic workflow is as follows:

• Preprocessing: The captcha images are preprocessed to ensure they're ready for the neural network. This includes resizing, converting to grayscale, and removing noise.

• Model Training: The CNN model is trained using labeled captcha data to recognize text in the images.

• Prediction: Once trained, the model is capable of solving new captchas by predicting the characters within them.

• Evaluation: The performance of the model is evaluated based on its accuracy in solving test captchas.

Getting Started

To get this project up and running locally, follow these steps:

Clone the repository to your local machine:

git clone https://github.com/your-username/captcha-solving-cnn.git

Install the required dependencies:

pip install -r requirements.txt

Download the captcha dataset (or use your own) and place it in the data/ folder.

Run the Code To train, test and run the model, simply run:

python solver.py

Inspiration

I was inspired to do this project by a fantastic blog post that explained how to use CNNs to recognize captchas. Check it out here: https://shorturl.at/GDTrG

Contact

If you have any questions or feedback, feel free to reach out!

Twitter: https://x.com/prethish_kumar LinkedIn: https://www.linkedin.com/in/prethish-kumar/ Email: [email protected]

Looking forward to hearing from you! 😄