Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550/spl times/3300 image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise.

Document representation and its application to page decomposition

Page segmentation using texture analysis

We present an algorithm for layout-independent document page segmentation based on document texture using multiscale feature vectors and fuzzy local decision information. Multiscale feature vectors are classified locally using a neural network to allow soft/fuzzy multi-class membership assignments. Segmentation is performed by integrating soft local decision vectors to reduce their "ambiguities".

Multiscale segmentation of unstructured document pages using soft decision integration

Pink Panther: a Complete Environment for Ground-Truthing and Benchmarking Document Page Segmentation

The paper presents an efficient technique for document page layout structure extraction and classification by analyzing the spatial configuration of the bounding boxes of different entities on the given image. The algorithm segments an image into a list of homogeneous zones. The classification algorithm labels each zone as test, table, line-drawing, halftone, ruling, or noise. The text lines and words are extracted within text zones and neighboring text lines are merged to form text blocks. The tabular structure is further decomposed into row and column items. Finally, the document layout hierarchy is produced from these extracted entities.

Document layout structure extraction using bounding boxes of different entitles

A method for automatically evaluating the quality of document page segmentation algorithms is introduced. Many different zoning techniques are now available but there is no robust method available to benchmark and evaluate them reliably. Our proposed strategy is a region-based approach, in which segmentation results are compared with manually generated "ground truth files", describing all possible correct segmentations. A segmentation ground truthing scheme has been proposed. The evaluation of segmentation quality is achieved by testing the overlap between the two sets of regions. In fact, the regions are defined as the "black" pixels contained in the extracted polygons. An explicit specification of segmentation errors and a numerical evaluation are derived. The algorithm is simple and fast, and provides a multi-level output for each segmentation. >

/pdf/benchmarking-page-segmentation-algorithms-35bzeirfxp.pdf

Randriamasy

Papers

Benchmarking page segmentation algorithms