Images: A dataset and texture-feature based performance evaluation

Abstract

Recognizing text with occlusion and perspective distortion in natural scenes is a challenging problem. In this work, we present a dataset of multi-lingual scripts and performance evaluation of script identification in this dataset using texture features. A ‘Station Signboard’ database that contains railway sign-boards written in 5 different Indic scripts is presented in this work. The images contain challenges like occlusion, perspective distortion, illumination effect, etc. We have collected a total of 500 images and corresponding ground-truths are made in semiautomatic way. Next, a script identification technique is proposed for multi-lingual scene text recognition. Considering the inherent problems in scene images, local texture features are used for feature extraction and SVMclassifier, is employed for script identification. From the preliminary experiment, the performance of script identification is found to be 84% using LBP feature with SVM classifier. © Springer Science+Business Media Singapore 2017.