Graphics recognition from binary images: one step or two steps

Recognizing graphic objects from binary images is an important task in many real-life applications. Generally, there are two ways to do the graphics recognition: one-step methods and two-step methods. The former recognizes graphic objects from binary images directly, while the latter consists of vectorization and postprocessing. Neither of them is perfect enough to handle all difficulties. The paper first reviews popular graphics recognition methods to understand their advantages and disadvantages. Next, the performance comparison between two classes of methods is made in two important aspects: the time efficiency and the graphics quality, and the experimental results of time-efficiency comparison of 7 popular methods are also reported. Finally, we propose a new hybrid graphics-recognition paradigm to integrate the advantages of both one-step methods and two-step methods and minimize their disadvantages. The proposed paradigm is capable of recognizing straight lines, arcs, circles and curves efficiently, and is helpful for extracting text images in text-graphics touching cases.

[1]  V. Kovalevsky,et al.  New definition and fast recognition of digital straight segments and arcs , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[2]  Dov Dori,et al.  A Generic Integrated Line Detection Algorithm and Its Object-Process Specification , 1998, Comput. Vis. Image Underst..

[3]  Theo Pavlidis,et al.  A vectorizer and feature extractor for document recognition , 1986 .

[4]  Ching Y. Suen,et al.  Thinning Methodologies - A Comprehensive Survey , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Karl Tombre Analysis of Engineering Drawings: State of the Art and Challenges , 1997, GREC.

[6]  Osamu Hori,et al.  Raster-to-vector conversion by line fitting based on contours and skeletons , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[7]  Karl Tombre,et al.  Graphics Recognition Algorithms and Systems , 1997, Lecture Notes in Computer Science.

[8]  John Y. Chiang,et al.  A New Algorithm For Line Image Vectorization , 1998, Pattern Recognit..

[9]  Atul K. Chhabra,et al.  Symbol Recognition : An Overview , 2005 .

[10]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[11]  Salvatore Tabbone,et al.  Vectorization in graphics recognition: to thin or not to thin , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[12]  Shijie Cai,et al.  Line net global vectorization: an algorithm and its performance evaluation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[13]  Chin-Chuan Han,et al.  Skeleton generation of engineering drawings via contour matching , 1994, Pattern Recognit..

[14]  Thomas Risse,et al.  Hough transform for line recognition: Complexity of evidence accumulation and cluster detection , 1989, Comput. Vis. Graph. Image Process..

[15]  Dov Dori,et al.  Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation , 1999, IEEE Trans. Pattern Anal. Mach. Intell..