An ingenious technique for symbol identification from high noise CAPTCHA images

This paper examines the problem of decoding a unique CAPTCHA that has very high noise levels with only partially visible symbols with variable spacing but no skewing. An ingenious method for decoding is proposed that starts with preprocessing the image and identifies symbols first before removing them from the image, unlike a number of existing methods. The algorithm is expected to be very fast owing to a reduction of the image matrix into a number of small segments that codify information in a lossy way that still allows for template matching for symbol identification. Character identification is attempted using both a Neural Network based approach and a mean square error method and their performance is compared and it is shown that the latter is significantly faster without being much less accurate. The CAPTCHA decoding strategy should offer insight into better methods of designing CAPTCHA´s and into decoding strategies for other applications such as OCR.