Robust Processing of Visual Contents for Text Recognition in Surveillance Applications
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초록
Textual information in natural scene images is an essential clue for several image-based applications. However, recognizing accurate text from natural scene images is a challenging task due to variation in illumination, text skewness, and variation in color, size, font, and camera alignment. In this paper, an optimal pre-processing pipeline is proposed for efficient and effective text recognition in visual surveillance applications. The pre-processing steps involve image de-sampling, de-noising, color to gray conversion, and adaptive thresholding. Furthermore, maximally stable extremal region (MSER) text detection algorithm has been used to detect textual regions which are then forwarded to the text recognition module. The recognized text by OCR is matched with a lookup table and the most likely value is extracted. Through experiments conducted for both Android and PC platforms using various datasets, it was concluded that the proposed method is more suitable for text recognition in surveillance applications.
Abstract
Textual information in natural scene images is an essential clue for several image-based applications. However, recognizing accurate text from natural scene images is a challenging task due to variation in illumination, text skewness, and variation in color, size, font, and camera alignment. In this paper, an optimal pre-processing pipeline is proposed for efficient and effective text recognition in visual surveillance applications. The pre-processing steps involve image de-sampling, de-noising, color to gray conversion, and adaptive thresholding. Furthermore, maximally stable extremal region (MSER) text detection algorithm has been used to detect textual regions which are then forwarded to the text recognition module. The recognized text by OCR is matched with a lookup table and the most likely value is extracted. Through experiments conducted for both Android and PC platforms using various datasets, it was concluded that the proposed method is more suitable for text recognition in surveillance applications.
참고문헌 (29)
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