Multi-level Fuzzy Based Renyi Entropy for Linguistic Classification of Texts in Natural Scene Images

Abstract

This paper focuses on linguistic classification of scene texts in natural scene images. In this paper, an attempt is made to localize texts based on multi-level thresholding by fuzzy-based Renyi entropy. Complex natural scene images with diversified challenges are considered. A set of heuristic rules comprising geometric filters and stroke width transform govern the process of locating potential text regions. The scene images may contain more than one language, where text recognition by optical character recognition system becomes challenging. Manual intervention is needed to specify the language of each text. To overcome this hurdle, linguistic classification of text regions is suggested in this paper. The proposed method is validated using publicly available dataset—MSRA-TD500. Results show that fuzzy-based Renyi entropy thresholding is able to segment the foreground text from complex natural scene images. Geometric filters could capture the inherent uniformity of the text. Stroke width transform eliminates the non-text regions. The performance measures such as precision, recall and F-measures are 78%, 77% and 76%, respectively. This shows the ability of the algorithm to extract the text from the scenes. The geometric feature such as area and corner shows better variation in discriminating the linguistic texts. Further, the first three Hu moment features also contribute remarkable role in analyzing the shape of extracted text regions. The classifier based on support vector machine (SVM) yields classification accuracy of 85.45% in discriminating English and Chinese alphabets. Area under the ROC curve (AUC) is 0.851 for SVM classifier. The proposed methodology has proved its robustness against common degradations, such as uneven illumination, varying font characteristics and blurring effects. Experimental results show that our method achieves better performance in linguistic classification.