Automatic writer identification framework for online handwritten documents using character prototypes

This paper proposes an automatic text-independent writer identification framework that integrates an industrial handwriting recognition system, which is used to perform an automatic segmentation of an online handwritten document at the character level. Subsequently, a fuzzy c-means approach is adopted to estimate statistical distributions of character prototypes on an alphabet basis. These distributions model the unique handwriting styles of the writers. The proposed system attained an accuracy of 99.2% when retrieved from a database of 120 writers. The only limitation is that a minimum length of text needs to be present in the document in order for sufficient accuracy to be achieved. We have found that this minimum length of text is about 160 characters or approximately equivalent to 3 lines of text. In addition, the discriminative power of different alphabets on the accuracy is also reported.     

Binarization, character extraction, and writer identification of historical Hebrew calligraphy documents

We present our work on the paleographic analysis and recognition system intended for processing of historical Hebrew calligraphy documents. The main goal is to analyze documents of different writing styles in order to identify the locations, dates, and writers of test documents. Using interactive software tools, a data base of extracted characters has been established. It now contains about 20,000 characters of 34 different writers, and will be distinctly expanded in the near future. Preliminary results of automatic extraction of pre-specified letters using the erosion operator are presented. We further propose and test topological features for handwriting style classification based on a selected subset of the Hebrew alphabet. A writer identification experiment using 34 writers yielded 100% correct classification.     

基于模糊模型相似测量的字符无监督分类法

该文提出一种基于模糊模型相似测量的文本分析系统的字符预分类方法 ,用于对字符的无监督分类 ,以提高整个字符识别系统的速度、正确性和鲁棒性 .作者在字符印刷结构归类的基础上 ,采用模板匹配方法将各类字符分别转换成基于一非线性加权相似函数的模糊样板集合 .模糊字符的无监督分类是字符匹配的一种自然范例并发展了加权模糊相似测量的研究 .该文讨论了该模糊模型的特性、模糊样板匹配的规则 ,并用于加快字符分类处理 ,经过字符分类 ,在字符识别时由于只需针对较小的模糊样板集合而变得容易和快速     

Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters

This paper describes an adaptive recognition system for isolated handwritten characters and the experiments carried out with it. The characters used in our experiments are alphanumeric characters, including both the upper- and lower-case versions of the Latin alphabets and three Scandinavian diacriticals. The writers are allowed to use their own natural style of writing. The recognition system is based on the k-nearest neighbor rule. The six character similarity measures applied by the system are all based on dynamic time warping. The aim of the first experiments is to choose the best combination of the simple preprocessing and normalization operations and the dissimilarity measure for a multi-writer system. However, the main focus of the work is on online adaptation. The purpose of the adaptations is to turn a writer-independent system into writer-dependent and increase recognition performance. The adaptation is carried out by modifying the prototype set of the classifier according to its recognition performance and the user's writing style. The ways of adaptation include: (1) adding new prototypes; (2) inactivating confusing prototypes; and (3) reshaping existing prototypes. The reshaping algorithm is based on the Learning Vector Quantization. Four different adaptation strategies, according to which the modifications of the prototype set are performed, have been studied both offline and online. Adaptation is carried out in a self-supervised fashion during normal use and thus remains unnoticed by the user. Received June 30, 1999 / Revised September 29, 2000     

基于局部结构分割的维吾尔文笔迹鉴别方法

针对基于纹理分析的全局特征提取受文本组合或字符连接影响较大,导致特征提取不稳定的现象,以及现有局部特征提取方法存在的不足,提出一种基于局部结构分割构建Codebook的维吾尔文文本无关笔迹鉴别方法。该方法根据笔迹图像的像素值提取维吾尔文笔迹样本中具有代表性的轮廓,生成描述书写者书写风格的Codebook并进行相似性度量,从而达到笔迹鉴别的目的。实验结果表明,该方法对于维吾尔文笔迹是一种简单、可行,具有较高识别率的笔迹鉴别方法。     

An Unsupervised Writer Identification Based on Generating ClusterableEmbeddings

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecture-based Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.     

基于形状分割的手写汉字笔划提取方法

笔迹鉴别的目的是区分不同的书写者,而笔划提取是笔迹鉴别的基础。本文提出了一种用于笔迹鉴别的手写汉字笔划提取算法,该算法定义了凹凸点与四种基本笔划相交类型的对应关系,通过字符图像轮廓上的凹凸点检测来确定笔划相交区域和相交类型;接着,在各个相交区域上,根据其笔划相交类型进行形状分割;最后,用对笔划轮廓两侧对应点进行跟踪的方法来进行细化。我们将该算法与基于细化和基于段化的笔划提取算法进行比较,实验结果表明,该提取算法具有比较高的准确率和有效性,因此本文提出的基于形状分割的手写汉字笔划提取方法具有较高的可操作性和实用价值。