A Gaussian Mixture Model for Binarization of Natural Scene Text

• 발행기관: 한국스마트미디어학회
• 저자명: Tran Anh Khoa Gueesang Lee
• 간행물 정보: 『스마트미디어저널』Vol2, No.2, 14~19쪽, 전체 6쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2013.06.30

영문 초록

Recently, due to the increase of the use of scanned images, the text segmentation techniques, which play critical role to optimize the quality of the scanned images, are required to be updated and advanced. In this study, an algorithm has been developed based on the modification of Gaussian mixture model (GMM) by integrating the calculation of Gaussian detection gradient and the estimation of the number clusters. The experimental results show an efficient method for text segmentation in natural scenes such as storefronts, street signs, scanned journals and newspapers at different size, shape or color of texts in condition of lighting changes and complex background. These indicate that our model algorithm and research approach can address various issues, which are still limitations of other senior algorithms and methods.

목차

Abstract

I. INTRODUCTION

II. METHODS

III. EXPERIMENTAL RESULTS

IV. CONCLUSION

REFERENCES

키워드

Segmentation Gaussian Mixture Model Gaussian Detection Gradient