Handwritten Character Recognition using K-Means Clustering

1. Introduction

Both academic and industrial interest in handwritten character recognition have grown alongside the modernisation of urban traffic networks. The formulation relies on k-means clustering, which is well suited to the nonlinear nature of the problem. The proposed method reduces the error by about 59% compared with the conventional approach. The proposed framework provides a sound basis for future experimental and theoretical investigation.

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2. Materials and Methods

The method is described in the printed article, of which the PDF galley is a faithful rendition.

3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

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