A Robust and Real Time Approach for Scene Text Localization and Recognition in Image Processing

Abstract

The text localization and recognition in real-time scene text images is still a big issue in current applications. Mobile application and digitization in real world gives a vital and broad impact on real-time scene text images. However, the efficiency of recognition rate depends upon the text localization, i.e., higher the purity of text background segmentation and decomposition, higher the rate of accuracy for the image recognition. In this paper is presented a new scene-text detection algorithm based on stroke detection and Hog transform method. The method introduces an approach for character detection and recognition, which combines the advantages of Hog transform and connected component methods. Characters are detected and recognized on the basis of image regions which contain strokes of specific orientations in a specific relative position, where the strokes are efficiently detected by convolving the image gradient field with a set of oriented bar filters. The method was evaluated on a standard dataset consisting mostly real time images where it achieves state-of-the-art results in both text localization and recognition. The results clearly depict the higher bit of accuracy in terms of localization and recognition for a collected dataset.