TEXT DETECTION IN NATURE SCENE IMAGE BASED ON VORONOI DIAGRAM AND CONDITIONAL RANDOM FIELD

It is an arduous task to accurately and effectively detect text in natural scenes. A scene text detection method based on the conditional random field (CRF) framework is proposed. By using Bayesian inference to estimate the confidence of the text maximum region as a unary cost item, by using the Voronoi diagram to construct the CRF spatial neighborhood information, the graph model was constructed. The maximum flow algorithm was used to minimize the cost function to distinguish text from non-text Mark. The geometric characteristics of the characters were used to cluster them into rows. The experimental results show that the proposed algorithm is improved compared with the traditional MSER algorithm, and the accuracy rate of natural scene text detection can reach 87%.