A General Approach for Handwritten Digits Segmentation Using Spectral Clustering

作者：Chen, Cheng; Guo, Jun^*

来源：14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017-11-09 To 2017-11-15.

DOI：10.1109/ICDAR.2017.95

• segmentation
• digit
• Spectral Clustering
• SVM

In this paper, an approach is proposed to solve a classic segmentation problem on handwritten touching digit pairs using spectral clustering (SC). SC has appeared in many of the state-of-the-art algorithms on image grouping problems recently, while it is a challenging work to build similarities between each pixel. In this paper, support vector machine (SVM) is used to predict the affinity matrix in SC instead of designing a complex function, hence making it a general approach. Different from traditional methods which focus on finding the cutting points or lines, we treat the handwritten string segmentation as a graph partitioning problem, which enables us to separate those digits connected in a very complicated way. We also introduce a 'second-segmentation' to optimize the segmentation result, and find out that the whole algorithm is similar to a multi-layer perception (MLP). Experiment results show that the proposed approach performs satisfactorily with high correct rate while keeping its own advantages as a general approach.

• 出版日期2017
• 单位华东师范大学