Learning to detect tables in document images using line and text information

Thong Huynh-Van, Khuong Nguyen-An, Trinh Le Ba Khanh, Hyung-Jeong Yang, Tuan Anh Tran, Soo-Hyung Kim

Abstract: Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to readers in a structured manner. It is still a challenging problem due to the variety of table structures and the complexity of document layout. This paper presents a hybrid method consisting of three fundamental steps to detect table zones: classification of the regions, detection of the tables that constitute intersecting horizontal and vertical lines, and identification of the tables made up by only parallel lines. Experiments on the UW-III dataset show that the obtained results are very promising.