Lecture content classification tool

In this paper, we address the problem of content classification for chalkboard images. Unlike document images, classifying chalkboard content into different categories is a challenging task. The task gets even tougher with varying handwriting styles and arbitrary drawings. We therefore, propose a tool with a set of functions to distinguish equations from text and figures. A hybrid solution is proposed, consisting of, the state of the art support vector machine (SVM) and optical character recognition (OCR) for this purpose. Prior to feature extraction and classification, some preprocessing steps are performed to remove noise and to enhance the chalk contrast. Our initial experiment shows promising results of above 85% accuracy for chalkboard images. We later on applied our algorithm to MNIST database of handwritten digits, our created handwritten lower-case and upper-case characters and basic mathematical operators and obtained 96% accuracy.