Gaussian mixture modeling and learning of neighboring characters for multilingual text extraction in images

This paper proposes an approach based on the statistical modeling and learning of neighboring characters to extract multilingual texts in images. The case of three neighboring characters is represented as the Gaussian mixture model and discriminated from other cases by the corresponding 'pseudo-probability' defined under Bayes framework. Based on this modeling, text extraction is completed through labeling each connected component in the binary image as character or non-character according to its neighbors, where a mathematical morphology based method is introduced to detect and connect the separated parts of each character, and a Voronoi partition based method is advised to establish the neighborhoods of connected components. We further present a discriminative training algorithm based on the maximum-minimum similarity (MMS) criterion to estimate the parameters in the proposed text extraction approach. Experimental results in Chinese and English text extraction demonstrate the effectiveness of our approach trained with the MMS algorithm, which achieved the precision rate of 93.56% and the recall rate of 98.55% for the test data set. In the experiments, we also show that the MMS provides significant improvement of overall performance, compared with influential training criterions of the maximum likelihood (ML) and the maximum classification error (MCE).