Bayesian classifier for multi-oriented video text recognition system

Abstract

Developing an automatic system for recognizing video texts such as signboards, street names, room numbers, building names and hotels names is challenging due to low resolution, complex background, font or font size variations, and multiple orientations of texts. In this paper, we develop a new system to recognize video texts through binarization by introducing a Bayesian classifier. We explore wavelet decomposition and gradient sub-bands to enhance text information in video. The enhanced information is used in different ways to calculate the requirement of Bayesian classifier, such as a priori probability and conditional probabilities of text pixels to estimate the posterior probability automatically, which results in text components. Connected component analysis is then applied to restore missing text information before sending it to an OCR engine if any disconnection exists in the text components. Experimental results on video data, the benchmark ICDAR scene character data (camera images) and arbitrary orientation data (camera images) show that the proposed method outperforms existing baseline methods in terms of recognition rates at both character and pixel levels.