An Efficient Text Segmentation Technique Based on Naive Bayes Classifier
Mehdi Haji, S.D. Katebi · 2005
In this paper the Naive Bayes Classifier (NBC) is introduced for text segmentation. A set of training data is generated from a wide category of document images for learning the NBC. The images used for generating the training data include both machine-printed and handwritten text with different fonts, sizes, intensity values and background models. A small subset of the coefficients of a discrete cosine transformed image block is used to classify the block as text or non-text. The NBC decision threshold is optimized on a test set. Experiments carried out on unseen documents show promising results. A comparison with a well-established method for text segmentation indicates advantages of the proposed method.