使用基于边缘特征的写器识别

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2015-11-01 DOI:10.1109/ACPR.2015.7486537

Zhenyin Fan, Zhenhua Guo, Youbin Chen

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引用次数: 1

摘要

本文提出了一种新的笔迹识别方法，从笔迹的边缘点提取不同半径和边缘描述符的原始局部二值模式(LBP)。然后，我们对这些基于边缘的特征进行组合。实验结果表明，基于边缘点的特征组合优于从全文中提取的传统特征，可以在CVL和icdar2013数据集上获得最先进的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Writer identification using edge based features

In this paper we present a new method for writer identification, which extract original Local Binary Pattern(LBP) of different radius and Edge descriptors from the edge points of the handwriting. Then, we make combinations of these edge based features. Experimental results demonstrate that the combination of edge points based features outperform traditional features extracted from the whole text, which can get state-of-the-art performance on CVL andICDAR2013 datasets.

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2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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