Document Zone Content Classification Using Decision Tree and HMM

A document can be divided into zones on the basis of its content. For example, a zone can be either text or non-text. This paper describes an algorithm to classify each given document zone into one of nine different classes. Foreground and background features are studied. We used an optimized binary decision tree to estimate the maximum zone content class probability in one set while used Viterbi algorithm to find the optimal solution for a zone sequence in the other set. The training, pruning and testing data set for the algorithm include images drawn from the UWCDROM III document image database. The classifier is able to classify each given scientific and technical document zone into one of the nine classes, text classes (of font size pt and font size pt), math, table, halftone, map/drawing, ruling, logo, and others. A zone content classification performance evaluation protocol is proposed. Using this protocol, our algorithm accuracy is with a mean false alarm rate of .