TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Machine-Learning Based Approach for Extracting Logical Structure of a Styled Document |
| 영문제목(English Title) |
A Machine-Learning Based Approach for Extracting Logical Structure of a Styled Document |
| 저자(Author) |
Tae-young Kim
Suntae Kim
Sangchul Choi
Jeong-Ah Kim
Jae-Young Choi
Jong-Won Ko
Jee-Huong Lee
Youngwha Cho
|
| 원문수록처(Citation) |
VOL 11 NO. 02 PP. 1043 ~ 1056 (2017. 02) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
A styled document is a document that contains diverse decorating functions such as different font, colors, tables and images generally authored in a word processor (e.g., MS-WORD, Open Office). Compared to a plain-text document, a styled document enables a human to easily recognize a logical structure such as section, subsection and contents of a document. However, it is difficult for a computer to recognize the structure if a writer does not explicitly specify a type of an element by using the styling functions of a word processor. It is one of the obstacles to enhance document version management systems because they currently manage the document with a file as a unit, not the document elements as a management unit. This paper proposes a machine learning based approach to analyzing the logical structure of a styled document composing of sections, subsections and contents. We first suggest a feature vector for characterizing document elements from a styled document, composing of eight features such as font size, indentation and period, each of which is a frequently discovered item in a styled document. Then, we trained machine learning classifiers such as Random Forest and Support Vector Machine using the suggested feature vector. The trained classifiers are used to automatically identify logical structure of a styled document. Our experiment obtained 92.78% of precision and 94.02% of recall for analyzing the logical structure of 50 styled documents.
|
| 키워드(Keyword) |
Logical Structure Analysis
Machine Learning
Feature Vector
Document Management System
|
| 파일첨부 |
PDF 다운로드
|
