论文标题
基于光谱图的特征，用于识别手写字符：手写devanagari数字的案例研究
Spectral Graph-based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals
论文作者
论文摘要
解释不同的写作风格，不同原始部分之间的不受约束的粗略性和关系是识别手写字符的必不可少的任务。由于特征表示不足，对手写字符的适当解释/描述似乎是一项具有挑战性的任务。尽管手写字符的现有研究是广泛的，但要在功能领域中有效代表角色仍然是一个挑战。在本文中，我们试图通过提出一种利用可靠的图表表示和频谱图嵌入概念来表征和有效地表示手写字符的方法来解决这些问题，从而考虑了写作样式，草书和关系。为了证实所提出方法的功效，在标准手写的数字计算机视觉模式识别（印度统计研究所加尔各答数据集的单位）上进行了广泛的实验。实验结果证明了有希望的发现，可以在以后的研究中使用。
Interpretation of different writing styles, unconstrained cursiveness and relationship between different primitive parts is an essential and challenging task for recognition of handwritten characters. As feature representation is inadequate, appropriate interpretation/description of handwritten characters seems to be a challenging task. Although existing research in handwritten characters is extensive, it still remains a challenge to get the effective representation of characters in feature space. In this paper, we make an attempt to circumvent these problems by proposing an approach that exploits the robust graph representation and spectral graph embedding concept to characterise and effectively represent handwritten characters, taking into account writing styles, cursiveness and relationships. For corroboration of the efficacy of the proposed method, extensive experiments were carried out on the standard handwritten numeral Computer Vision Pattern Recognition, Unit of Indian Statistical Institute Kolkata dataset. The experimental results demonstrate promising findings, which can be used in future studies.
