Table Detection in Document Images using Foreground and Background Features

Abstract

Table detection is an important step in many document analysis systems. It is a difficult problem due to the variety of table layouts, encoding techniques and the similarity of tabular regions with non-tabular document elements. Earlier approaches of table detection are based on heuristic rules or require additional PDF metadata. Recently proposed methods based on machine learning have shown good results. This paper demonstrates performance improvement to these table detection techniques. The proposed solution is based on the observation that tables tend to contain more numeric data and hence it applies color coding/coloration as a signal for telling apart numeric and textual data. Deep learning based Faster R-CNN is used for detection of tabular regions from document images. To gauge the performance of our proposed solution, publicly available UNLV dataset is used. Performance measures indicate improvement when compared with best in-class strategies.