Segmentation of connected characters in text-based CAPTCHAs for intelligent character recognition

摘要

Over last few years, CAPTCHAs are ubiquitously found on internet as a security mechanism to distinguish between humans and spams. The text-based CAPTCHAs offer users to recognize the distorted text from the challenged images. Having based on hard AI problem, they have emerged as a hot research topic in computer vision and machine learning. The contemporary text-based CAPTCHAs are based on the segmentation problem that involves their decomposition into sub-images of individual characters. This is a challenging task for current OCR programs which is not yet solved to a great extent. In this paper, we present a novel segmentation and recognition method which uses simple image processing techniques including thresholding, thinning and pixel count methods along with an artificial neural network for text-based CAPTCHAs. We attack the popular CCT (Crowded Characters Together) based CAPTCHAs and compare our results with other schemes. As overall, our system achieves an overall precision of 51.3, 27.1 and 53.2% for Taobao, MSN and eBay datasets with 1000,500 and 1000 CAPTCHAs respectively. The benefits of this research are twofold: by recognizing text-based CAPTCHAs, we not only explore the weaknesses in the current design but also find a way to segment and recognize the connected characters from images. The proposed algorithm can be used in digitization of ancient books, handwriting recognition and other similar tasks.