Segmentation of Heterogeneous Documents into Homogeneous Components using Morphological Operations

Abstract

The research on document layout analysis has been widespread over a large arena recently and is craving for more efficiency day by day. Document segmentation is an important preprocessing step before analyzing the layouts. This paper presents a language-independent document segmentation system that segments a heterogeneous printed document into homogeneous components like halftones and graphics, texts and tables including its individual cells. From an input document page homogeneous components are segmented in three steps with three separate modules, which are- extraction of halftone images, extraction of tables and segmentation of text blocks. These modules altogether build the whole page segmentation system which takes an input image of heterogeneous document page and produces an output with explicitly indicated homogeneous segments with colored bounding boxes. The modules use morphological operations to detect the components. To improve the performance of image segmentation Residual Image Fragments Retrieval (RIFR) is proposed. The paper also proposes Text Extraction from Table Cells (TETC). Combining RIFR and TETC together we get an overall accuracy of 93%. Table and cell detection have a higher accuracy of 96% whereas image and texts have around 90% accuracy.