LoG and Structural Based Arbitrary Oriented Multilingual Text Detection in Images/Video

Abstract

Text in an image or a video affords more precise meaning and text is a prominent source with a clear explanation of the content than any other high-level or low-level features. The text detection process is a still challenging research work in the field of computer vision. However, complex background and orientation of the text leads to extremely stimulating text detection tasks. Multilingual text consists of different geometrical shapes than a single language. In this article, a simple and yet effective approach is presented to detect the text from an arbitrary oriented multilingual image and video. The proposed method employs the Laplacian of Gaussian to identify the potential text information. The double line structure analysis is applied to extract the true text candidates. The proposed method is evaluated on five datasets: Hua's, arbitrarily oriented, multi-script robust reading competition (MRRC), MSRA and video datasets with performance measures precision, recall and f-measure. The proposed method is also tested on real-time video, and the result is promising and encouraging.