Script Identification from Camera-Captured Multi-script Scene Text Components

Identification of script from multi-script text components of camera-captured images is an emerging research field. Here, challenges are mainly twofold: (1) typical challenges of camera-captured images like blur, uneven illumination, complex background, etc., and (2) challenges related to shape, size, and orientation of the texts written in different scripts. In this work, an effective set consisting of both shape-based and texture-based features is designed for script classification. An in-house scene text data set comprising 300 text boxes written in three scripts, namely Bangla, Devanagri, and Roman is prepared. Performance of this feature set is associated with five popular classifiers and highest accuracy of 90% is achieved with Multi-layer Perceptron (MLP) classifier, which is reasonably satisfactory considering the domain complexity.