Video Script Identification Using a Combination of Textural Features

This paper presents a system for script recognition of the text appearing in video frames. The textual content in videos is generally extracted and recognized for development of text based indexing and retrieval systems. If the text in videos appears only in a single script, the output of text detector is directly fed to a video Optical Character Recognition (OCR) system for recognition. However, in cases where text may appear in multiple scripts, a script recognition module is required to recognize the script of the text so that it can be processed by the respective OCR. We propose a video script recognition system that considers text in each script as a unique texture. A number of texture measures are extracted from text blocks and an artificial neural network is trained to learn to distinguish between different scripts. The system evaluated on video text blocks in five different scripts (Arabic, English, Urdu, Hindi and Chinese) reported promising recognition rates. In addition to the performance of individual textural features, different combinations of texture measures were investigated which realized interesting results.