CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
BINARIZATION OF NATURAL SCENE TEXT BASED ON L1-NORM PCA
Bai JF(白锦峰)
2013
Conference Name2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
Conference Date15-19 July 2013
Conference PlaceSan Jose, CA, USA
Abstract

In this paper, a novel binarization technique is introduced for natural scene text, which can be applied after the text location step in order to improve OCR recognition. At the first step, an “optimum” conversion from color image to grayscale im- age is performed by minimizing L1 N orm distance between original color image and reconstructed image on correspond- ing optimum projection vector. Based on it, at the second step, an approach is developed to classify scene text into two categories: “simple” and “complex”, for the purpose of opti- mizing the processing speed and preserving performance. At the last step, binarization is performed with different methods for “simple” and “complex” scene text. Results on word im- ages from the challenging ICDAR 2003 dataset show that our scheme can gain higher performance compared with state-of- the-art methods in OCR accuracy.


Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21514
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Recommended Citation
GB/T 7714
Bai JF. BINARIZATION OF NATURAL SCENE TEXT BASED ON L1-NORM PCA[C],2013.
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