CAPTCHA RECOGNITION USING DCNN

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for websites to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA technology By using the concept of deep learning and computer vision, the very purpose of the CAPTCHAs can be defeated. This test can be passed automatically with the help of Convolutional Neural networks(CNN). A CNN is an algorithm of deep learning which takes an image as input and then assigns some value to various features in the image which further helps to differentiate one feature from the other. Its main purpose is to transform the images into a form which is much easier to process, without losing features which are essential for getting an optimized prediction. The proposed system for this project is to expand this CAPTCHA recognition system for larger and more noisy CAPTCHA containing all the symbols possible, a method based on the Deep Convolutional Neural Network (DCNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion. The future scope of this project lies in technologies where more noisier images can be processed such as license plates, handwriting recognition etc.