Text detection in natural scene images using two masks filtering

Abstract

Text detection in natural scenes holds great importance in the field of research and still remains a challenge because of size, various fonts, line orientation, different illumination conditions, weak character and complex background in image. The contribution of the proposed method is filtering out complex backgrounds by utilizing two masks filtering based on text confidence map in the first step and multi-channel maximally stable extremal regions (MSERs) in the second step. Both steps are designed to enhancement, maximize capacity of zones text pixels candidates to distinguish text boxes from the rest of the image. Then non-text components are filtered by the classification of character candidate based on Support Vector Machines (SVM) using HOG features. The false positives are eliminated by geometrical properties of text blocks. Finally we apply boundary box localization after a stage of word grouping. The proposed method has been evaluated on ICDAR 2013 scene text detection competition dataset and the encouraging experiments results demonstrate the robustness of our method.