GOAL: Towards Understanding of Graphic Objects from Architectural to Line Drawings

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.

[1]  Yalin Wang,et al.  Document zone content classification and its performance evaluation , 2006, Pattern Recognit..

[2]  Tuan D. Pham.  Unconstrained logo detection in document images , 2003, Pattern Recognit..

[3]  Partha Bhowmick,et al.  Construction of isothetic covers of a digital object: A combinatorial approach , 2010, J. Vis. Commun. Image Represent..

[4]  Jingying Chen,et al.  Noisy logo recognition using line segment Hausdorff distance , 2003, Pattern Recognit..

[5]  Tim Ritchings,et al.  Representation and classification of complex-shaped printed regions using white tiles , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[6]  Robert P. Futrelle,et al.  Extraction,layout analysis and classification of diagrams in PDF documents , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[7]  Robert M. Haralick,et al.  Document image understanding: geometric and logical layout , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Hong Yan,et al.  Text region extraction in a document image based on the Delaunay tessellation , 2003, Pattern Recognit..

[9]  Jean-Yves Ramel,et al.  Strategy for Line Drawing Understanding , 2003, GREC.

[10]  Shijie Cai,et al.  An Object-Oriented Progressive-Simplification-Based Vectorization System for Engineering Drawings: Model, Algorithm, and Performance , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Richard Zanibbi,et al.  Recognizing Mathematical Expressions Using Tree Transformation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Daniel P. Lopresti,et al.  Evaluating the performance of table processing algorithms , 2002, International Journal on Document Analysis and Recognition.

[13]  Jing Liu,et al.  Sketch Parameterization Using Curve Approximation , 2005, GREC.

[14]  Philip A. Chou,et al.  Document Image Decoding Using Markov Source Models , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Wenyin Liu.  On-line Graphics Recognition: State-of-the-Art , 2003, GREC.

[16]  Azriel Rosenfeld,et al.  Digital geometry - geometric methods for digital picture analysis , 2004 .

[17]  Robert M. Gray,et al.  Image classification by a two-dimensional hidden Markov model , 2000, IEEE Trans. Signal Process..