A Caption Text Detection Method from Images/Videos for Efficient Indexing and Retrieval of Multimedia Data

Textual information embedded in multimedia can provide a vital tool for indexing and retrieval. Text extraction process has many inherent problems due to the variation in font sizes, color, backgrounds and resolution. Text detection and localization are the most challenging phases of text extraction process whereas text extraction results are highly dependent upon these phases. This paper focuses on the text localization because of its very fundamental importance. Two effective feature vectors are introduced for the classification of the text and nontext objects. First feature vector is represented by the Radon transform of text candidate objects. Second feature vector is derived from the detailed geometrical analysis of text contents. Union of two feature vectors is used for the classification of text and nontext objects using support vector machine (SVM). Text detection and localization results are evaluated on two publicly available datasets namely ICDAR 2013 and IPC-Artificial text. Moreover, results are compared with state-of-the-art techniques and the Comparison demonstrates the superiority of the presented research.