Optical Braille Recognition with Haar Wavelet Features and Support-Vector Machine
This paper proposes a Braille character recognition system, based on Haar feature extraction and SupportVector Machine (SVM) classification. Braille documents are first scanned into full-color image. The images are then passed through a preprocessor which converts the images into grayscale images, and performs geometric correction. Then a sliding window is applied to the image to crop out sub images. For each sub image, Haar feature vector is calculated and then sent to SVM to decide whether the sub image contains a Braille dot; this translates the original grayscale image into a binary image. Then a simple searching algorithm is applied to the binary image to translate Braille characters into English letters. This method is simple, convenient, and easy to operate, also able to extract dots online in real time. The experiments show that the method is effective and accurate for Braille extraction.
Optical Braille character recognition Haar wavelet feature Support-Vector Machine SVM
Jie Li Xiaoguang Yan Dayong Zhang
College of Electronic and Information Engineering Changchun University Changchun, Jilin Province, Ch 877 Shen Jia Nong Road, Shanghai,China
国际会议
长春
英文
64-67
2010-08-24(万方平台首次上网日期,不代表论文的发表时间)