Classifier comparison for MSER-based text classification in scene images

Khalid Iqbal, Xu-Cheng Yin, Xuwang Yin, Hazrat Ali, Hong-Wei Hao

Abstract: Text detection in images is an emerging area of interest with a growing motivation to researchers. Various methodologies have been developed to localize text contained in scene images. One main application of localizing scene image text is to produce a real time support to visually impaired persons. To design a real-time support platform for visually impaired persons, classification of textual information, i.e. character and non-character information can provide a baseline for further research. However, the challenge exists in choosing the optimum classifier for this purpose. In this work, first, we used Maximally Stable Extremal Regions (MSERs) to detect character candidates in a scene image; then, we trained several classifiers, i.e., AdaboostM1, Bayesian Logistic Regression, Naïve Bayes, and Bayes Net, to classify MSERs as characters and non-characters; and finally, we compared and analyzed the performances of these classifiers empirically. From experiments, it has been concluded that Bayesian Logistic Regression provides the better accuracy over the other three classifiers. This work argues that MSER based character candidates extraction and Bayesian Logistic Regression based text classification are two prominent and potential techniques in scene text detection.