Abstract
The handwriting of an individual may vary substantially with factors such as mood, time, space, writing speed, writing medium and tool, writing topic, etc. It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of a person, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person. However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting. In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing. Here, we work on writer identification/verification from offline Bengali handwriting of high intra-variability. To this end, we use various models mainly based on handcrafted features with SVM (Support Vector Machine) and features auto-derived by the convolutional network. For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique. We have obtained some interesting results.
Abstract (translated)
个人的笔迹可能会因情绪，时间，空间，写作速度，书写媒介和工具，写作主题等因素而发生很大变化。对特定的手写模式集执行自动编写器验证/识别变得具有挑战性（例如，一个人的快速手写，特别是当使用同一个人的不同写作模式（例如，正常速度）训练系统时。然而，通过实验分析是否存在对高内变量笔迹不敏感的个性的任何隐含特征将是有趣的。在本文中，我们研究了一些手工制作的特征和从变量内写作中提取的自动派生特征。在这里，我们致力于作者识别/验证从离线孟加拉语的高内部变异手写。为此，我们使用主要基于SVM（支持向量机）的手工特征的各种模型以及由卷积网络自动导出的特征。为了实验，我们从两个不同的100个作者集合生成了两个手写数据库，并通过数据增强技术扩大了数据集。我们获得了一些有趣的结果。
URL
https://arxiv.org/abs/1708.03361
