For my independent study, I investigated optical character recognition techniques and their application to recognizing text-based HIPs (methods used to distinguish human users and machines on the internet). This study is an extension of methods covered in neural networks and machine learning, computer vision, and artificial intelligence. The report includes experimental results of breaking the ASP Security Image Generator (CAPTCHA) v2.0 with a 72% success rate. Posting of source code is not currently planned. However, my paper contains fairly detailed steps and can be downloaded here.
As part of my Artifical Intelligence course, we developed a rule-based expert system that can autonomously govern a building’s environment to optimize user comfort and energy consumption, whilst providing safety and monitoring functions. The expert system has been developed using the Java programming language and the Java Expert System Shell (JESS). Rules are stored as an external resource and can be modified in real time without requiring a rebuild of the entire project. Write-up 1 includes problem description, design considerations, and implementation details. Write-up 2 includes testing results and a comparison to another system.
Optical character recognition refers to the process of translating images of hand-written, typewritten, or printed text into a format understood by machines for the purpose of editing, indexing/searching, and a reduction in storage size. The OCR process is complicated by noisy inputs, image distortion, and differences between typefaces, sizes, and fonts. Artificial neural networks are commonly used to perform character recognition due to their high noise tolerance. In my Artificial Intelligence course, I explored several OCR techniques which utilize ANN’s.
My final writeup where I surveyed four OCR techniques which utilized ANNs can be downloaded here.
I also gave a final presentation on my research where I compared and contrasted four methods. My slides can be downloaded here.