Hill Climbing Approach for Text Binarization from Videos

Objectives: To develop presents an efficient algorithm for binarization of text from videos. However, it is a challenging and difficult task in image processing, due to their complicated background and non uniform character size. Method/Analysis: This study focuses a novel approach for binarization of text region from videos based on Hill climbing Algorithm. Symmetric filtering and graph cut algorithms are used to refine the obtained clusters. Finally, an optimal clustering selection algorithm is applied to obtain the text region. Findings: The experimental results show that the proposed text binarization technique is robust in text detection with various character size and complicated background. Data sets are taken from the You Tube Video Text (YVT) harvested from YouTube. Application: This proposed binarization technique is applicable to automated licence plate recognition system, especially to identify Indian vehicles licence plates.