Recognition of overlaid Chinese characters in videos without binarization

doi:10.7523/j.issn.2095-6134.2018.03.015

Abstract

Abstract: In this paper, a new method for recognizing caption texts in videos is proposed. Due to varying font sizes, colors, styles, and resolutions and complex backgrounds in videos, it is still a challenging problem to recognize overlaid texts in videos. Most existing overlaid text recognition methods are based on the combination of text binarization and traditional OCR engine. However, the process of text binarization may incur noises and text stroke information loss. Additionally, techniques of traditional OCRs are mainly focused on high-resolution scans of printed documents, which have the characteristics of single color background, little noise, and more complete stroke information. Hence, traditional OCR engines might not be robust enough to recognize the binarization results of overlaid text images. In order to solve this problem, we directly extract Gabor features from overlaid text images without binarization for training the two-level character recognizer. The final experimental results demonstrate that the proposed method makes a great progress in overlaid Chinese text recognition with multiple fonts.

Key words: video overlaid text, OCR, Gabor, nearest prototype classifier

CLC Number:

TP393

TIAN Jie, WANG Weiqiang, SUN Yi. Recognition of overlaid Chinese characters in videos without binarization[J]. , 2018, 35(3): 402-408.

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