Multi-lingual Scene Text Detection by Local Histogram Analysis and Selection of Optimal Area for MSER

The problem of scene text detection has been quite intriguing for the research fraternity due to wide scope of applications. In recent times, some robust technique like Maximally Stable Extremal Region (MSER) has been developed and have gained immense popularity in detection and localization of text components in the wild. However, the problem of an optimal threshold and range of area in MSER has been addressed by very few researchers. In this work, we address this problem by dynamic thresholding through local histogram analysis and formulating a generalized range of the area for the MSER function. The detected MSERs are then analyzed by observing their region properties to distinguish text components from non-text ones and finally bounding the text components. The proposed technique is evaluated on a set of 200 images with multi-lingual text comprising English, Bangla, Hindi and Oriya languages.