Ez Japanese writer

Technical review

Ez Japanese writer     

射频识别系统中高频读写器的研究与开发

介绍了射频识别(RFID)系统中读写器的设计开发以及RFID系统中S6700芯片的通信协议,同时给出了读写器硬件系统的结构、底层驱动和软件工作流程。读写器采用ATMEL公司的ATmega128单片机和TI公司的13.56MHz频段下的多协议收发芯片S6700。     

Feature learning and encoding for multi-script writer identification

International Journal on Document Analysis and Recognition (IJDAR) - Writer identification from handwriting samples has been an interesting research problem for the pattern recognition community in...     

The theatre through the computer

There is a long history of the theatre being used with educational purposes. Now the evolution of multimedia resources provide an opportunity to implement theatre through the computer. Although software for the production of multimedia stories exist, they give little attention to the necessary interface aspects required for a virtual theatre implementation. The objective of this work is to present the Theatre in the Computer (TC); software especially developed for the education of children. This software can be used to produce and to distribute plays through the Internet. The educational possibilities of TC are discussed here.     

Approximate analysis of reader/writer queues

We analyze the performance of queues that serve readers and writers. Readers are served concurrently, while writers require exclusive service. We approximately analyze a first-come-first-serve (FCFS) reader/writer queue, and derive simple formulae for computing waiting times and capacity under the assumption of Poisson arrivals and exponential service. We extend the analysis to handle a one writer queue, and a queue that includes write intention locks. The simple analyses that we present can be used as rules of thumb for designing concurrent systems     

Novel geometric features for off-line writer identification

Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features.     

基于复小波的脱线手写体笔迹鉴别

进行脱线笔迹鉴别时,笔迹特征只能从手写体图像中提取,且无法获取书写时的动态信息,导致了脱线笔迹鉴别的正确率不是很高。为了进一步提高脱线手写体笔迹鉴别的正确率,提出基于复小波的GGD模型方法对笔迹进行鉴别。与传统小波GGD模型方法比较,复小波GGD模型方法具有时移不变性和良好的方向分析能力,在提取纹理特征方面更有效。实验结果表明,该方法在鉴别正确率上有很大的提升。     

Text-independent writer identification and verification using textural and allographic features

The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates     

The market and computer technology

What is the impact of economics on vendors of chips, systems, software, and applications? The author looks at the issues we all need to understand when considering the market and its impact on our technology. This essay reviews a few of the economic factors that shape the emergence, development, and “control” of an open standard     

Automatic writer identification from text line images

In the present article, new techniques have been introduced for revealing the individual features of a person??s handwriting pattern from the scanned images of handwritten text lines to facilitate text-independent writer identification. These techniques are aimed at designing a dynamic model which can be formalized according to any handwritten text line. Various combinations of the extracted features are applied to three well known classifiers for evaluating the contribution of features to define the correct identification rate. The K-NN, GMM, and Normal Density Discriminant Function Bayes classifiers are used in the present identification model. The experimental studies are conducted using two datasets obtained from the IAM database. The first dataset has already been proposed and used in the literature, whereas the second dataset is an expanded version of the first dataset and has been constituted for the first time in this study to analyze the performance of the extracted features under conditions such as an increased number of writers to discriminate in the database and a decreased number of text lines per writer. The remarkable identification rates obtained from the three classifiers on both datasets clearly indicate that the proposed feature extraction techniques can be effectively used in writer identification systems.