Machine Vision and Applications

, Volume 24, Issue 6, pp 1213–1227 | Cite as

Learning class-specific dictionaries for digit recognition from spherical surface of a 3D ball

Original Paper


In the literature, very few researches have addressed the problem of recognizing the digits placed on spherical surfaces, even though digit recognition has already attracted extensive attentions and been attacked from various directions. As a particular example of recognizing this kind of digits, in this paper, we introduce a digit ball detection and recognition system to recognize the digit appearing on a 3D ball. The so-called digit ball is the ball carrying Arabic number on its spherical surface. Our system works under weakly controlled environment to detect and recognize the digit balls for practical application, which requires the system to keep on working without recognition errors in a real-time manner. Two main challenges confront our system, one is how to accurately detect the balls and the other is how to deal with the arbitrary rotation of the balls. For the first one, we develop a novel method to detect the balls appearing in a single image and demonstrate its effectiveness even when the balls are densely placed. To circumvent the other challenge, we use spin image and polar image for the representation of the balls to achieve rotation-invariance advantage. Finally, we adopt a dictionary learning-based method for the recognition task. To evaluate our system, a series of experiments are performed on real-world digit ball images, and the results validate the effectiveness of our system, which achieves 100 % accuracy in the experiments.


Digit ball recognition Dictionary learning Sparse coding Circle detection Rotation invariance 



The authors are grateful to the anonymous reviewers for their excellent reviews and constructive comments that helped to improve the manuscript and our system. This work is supported by 973 Program (No.2010CB327904) and Natural Science Foundations (No.61071218) of China.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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