Accurate text localization in images based on SVM output scores

In this paper, we propose a new approach for accurate text localization in images based on SVM (support vector machine) output scores. In general, SVM output scores for the verification of text candidates provide a measure of the closeness to the text. Up to the present, most researchers used the score for verifying the text candidate region whether it is text or not. However, we use the output score for refining the initial localized text lines and selecting the best localization result from the different pyramid levels. By means of the proposed approach, we can obtain more accurate text localization results. Our method has three modules: (1) text candidate detection based on edge-CCA (connected component analysis), (2) text candidate verification based on the classifier fusion of N-gray (normalized gray intensity) and CGV (constant gradient variance), and (3) text line refinement based on the SVM output score, color distribution and prior geometric knowledge. By means of experiments on a large news database, we demonstrate that our method achieves impressive performance with respect to the accuracy, robustness and efficiency.