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首页> 外文会议>2018 International Conference on Intelligent Systems and Computer Vision >Effective feature descriptor-based new framework for off-line text-independent writer identification
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Effective feature descriptor-based new framework for off-line text-independent writer identification

机译：基于有效特征描述符的新框架，用于离线文本无关的作者识别

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摘要

Feature engineering is a key factor of machine learning applications. It is a fundamental process in writer identification of handwriting, which is an active and challenging field of research for many years. We propose a conceptually computationally efficient, yet simple and fast local descriptor referred to as Block Wise Local Binary Count (BW-LBC) for offline text-independent writer identification of handwritten documents. Proposed BW-LBC operator, which characterizes the writing style of each writer, is applied to a set of connected components extracted and cropped from scanned handwriting samples (documents or set of words/text lines) where each labeled component is seen as a texture image. The feature vectors computed from the components in all the writing samples are then fed to the 1NN (Nearest Neighbor) classifier to identify the writer of the query documents. Simulated experiments are performed on three challenging and publicly available handwritten databases (IFN/ENIT, AHTID/MW, and CVL) containing handwritten texts in Arabic and English languages, respectively. Experimental results show that our proposed system combined with BW-LBC descriptor demonstrate superior performance on the Arabic script and competitive performance on the English one against the old and recent writer identification systems of the state-of-the-art.
机译：特征工程是机器学习应用程序的关键因素。这是鉴定笔迹的一个基本过程，这是多年来活跃而富有挑战性的研究领域。我们提出了一种概念上高效的，但又简单又快速的本地描述符，称为“块明智本地二进制计数”（BW-LBC），用于手写文档的脱机文本无关作者识别。提议的BW-LBC运算符可表征每个作者的书写风格，该运算符应用于从扫描的手写样本（文档或单词/文本行集）中提取和裁剪的一组连接的组件，其中每个标记的组件都被视为纹理图像。然后将从所有书写样本中的组件计算出的特征向量馈送到1NN（最近邻居）分类器，以标识查询文档的作者。在三个具有挑战性且公开可用的手写数据库（IFN / ENIT，AHTID / MW和CVL）上进行了模拟实验，这些数据库分别包含阿拉伯语和英语的手写文本。实验结果表明，我们提出的系统与BW-LBC描述符相结合，在阿拉伯语文字上表现出卓越的性能，在英语水平上具有竞争优势，与最新和最先进的作家识别系统相比。

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