IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Image Decoding Using Markov Source Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Mathematical Expressions Using Tree Transformation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and classification of complex-shaped printed regions using white tiles
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Extraction, layout analysis and classification of diagrams in PDF documents
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Digital Geometry: Geometric Methods for Digital Picture Analysis
Digital Geometry: Geometric Methods for Digital Picture Analysis
Document zone content classification and its performance evaluation
Pattern Recognition
Construction of isothetic covers of a digital object: A combinatorial approach
Journal of Visual Communication and Image Representation
Sketch parameterization using curve approximation
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Understanding Digital Documents Using Gestalt Properties of Isothetic Components
International Journal of Digital Library Systems
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Understanding of graphic objects has become a problem of pertinence in today's context of digital documentation and document digitization, since graphic information in a document image may be present in several forms, such as engineering drawings, architectural plans, musical scores, tables, charts, extended objects, hand-drawn sketches, etc. There exist quite a few approaches for segmentation of graphics from text, and also a separate set of techniques for recognizing a graphics and its characteristic features. This paper introduces a novel geometric algorithm that performs the task of segmenting out all the graphic objects in a document image and subsequently also works as a high-level tool to classify various graphic types. Given a document image, it performs the text-graphics segmentation by analyzing the geometric features of the minimum-area isothetic polygonal covers of all the objects for varying grid spacing, g. As the shape and size of a polygonal cover depends on g, and each isothetic polygon is represented by an ordered sequence of its vertices, the spatial relationship of the polygons corresponding to a higher grid spacing with those corresponding to a lower spacing, is used for graphics segmentation and subsequent classification. Experimental results demonstrate its efficiency, elegance, and versatility.