Machine Printed and Handwritten Text Discrimination in Korean Document Images

• 발행기관: 한국스마트미디어학회
• 저자명: Son Tung Trieu Guee Sang Lee
• 간행물 정보: 『스마트미디어저널』Vol5, No.3, 1~5쪽, 전체 5쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2016.09.30

영문 초록

Nowadays, there are a lot of Korean documents, which often need to be identified in one of printed or handwritten text. Early methods for the identification use structural features, which can be simple and easy to apply to text of a specific font, but its performance depends on the font type and characteristics of the text. Recently, the bag-of-words model has been used for the identification, which can be invariant to changes in font size, distortions or modifications to the text. The method based on bag-of-words model includes three steps: word segmentation using connected component grouping, feature extraction, and finally classification using SVM(Support Vector Machine). In this paper, bag-of-words model based method is proposed using SURF(Speeded Up Robust Feature) for the identification of machine printed and handwritten text in Korean documents. The experiment shows that the proposed method outperforms methods based on structural features.

목차

I. INTRODUCTION

II. SIFT vs. SURF features

III. PROPOSED METHOD

IV. EXPERIMENTAL RESULTS

V. CONCLUSION

키워드

recognition|handwritten|machine printed|bag-of-words model|document analysis