印刷体藏文文字识别技术研究

藏文字因其结构的特殊性,在应用传统文字识别方法进行识别时正确识别率较低,识别效果较差。在深入分析以印刷体藏文文字特征的基础上,提出了一系列可以在干扰情况下提高识别率的方法,包括局部自适应二值化算法、基于连通域的切分、基于网格的模糊笔划特征提取等。实验结果说明,这些方法可提高印刷体藏文文字识别系统的正确识别率和抗干扰能力。     

Dual classifier system for handprinted alphanumeric character recognition

This paper presents a dual classifier handprinted character recognition system that is implemented using Radial Basis Function (RBF) networks. Each classifier in the system extracts a different set of features from the input character and makes its own indpendent classification decision. The features used are the diagonal and partitioned radial projections, and the four-directional edge maps of the mage. The system then combines these decisions before giving a final classification output. Several different methods of desinging the combiner are examined. The proposed system is tested on a database of handprinted alphanumeric characters, and the results are fourd to be very promising.     

Multifont size-resilient recognition system for Ethiopic script

This paper presents a novel framework for recognition of Ethiopic characters using structural and syntactic techniques. Graphically complex characters are represented by the spatial relationships of less complex primitives which form a unique set of patterns for each character. The spatial relationship is represented by a special tree structure which is also used to generate string patterns of primitives. Recognition is then achieved by matching the generated string pattern against each pattern in the alphabet knowledge-base built for this purpose. The recognition system tolerates variations on the parameters of characters like font type, size and style. Direction field tensor is used as a tool to extract structural features.     

Zoning methods for handwritten character recognition: A survey

This paper presents a survey on zoning methods for handwritten character recognition. Through the analysis of the relevant literature in the field, the most valuable zoning methods are presented in terms of both topologies and membership functions. Throughout the paper, diverse zoning topologies are presented based on both static and adaptive approaches. Concerning static approaches, uniform and non-uniform zoning strategies are discussed. When adaptive zonings are considered, manual and automatic strategies for optimal zoning design are illustrated as well as the most appropriate zoning representation techniques. In addition, the role of membership functions for zoning-based classification is highlighted and the diverse approaches to membership function selection are presented. Concerning global membership functions, the paper introduces order-based approaches as well as fuzzy approaches using border-based and ranked-based fuzzy membership values. Concerning local membership functions, the recent parameter-based approaches are described, in which the optimal membership-function is selected for each zone of the zoning method. Finally, a comparative analysis on the performance of zoning methods is presented and the most interesting approaches are focused on in terms of topology design and membership function selection. A list of selected references is provided as a useful tool for interested researchers working in the field.     

Offline handwritten Amharic word recognition

This paper describes two approaches for Amharic word recognition in unconstrained handwritten text using HMMs. The first approach builds word models from concatenated features of constituent characters and in the second method HMMs of constituent characters are concatenated to form word model. In both cases, the features used for training and recognition are a set of primitive strokes and their spatial relationships. The recognition system does not require segmentation of characters but requires text line detection and extraction of structural features, which is done by making use of direction field tensor. The performance of the recognition system is tested by a dataset of unconstrained handwritten documents collected from various sources, and promising results are obtained.     

The measurement of print quality for optical character recognition systems

The problem of assessing print quality in a way which will correlate with performance in a character recognition system is described with reference to existing specifications and measurement procedures. An instrument has been constructed which enables an operator to make measurements on a print sample quickly and easily, so as to acquire sufficient data for reliable statistics. Some results are quoted and their implications are discussed.     

An overview of character recognition methodologies

This work presents an overview of character recognition methodologies that have evolved in this century. At first the scanning devices that are used in character recognition will be explained, then some points will be stressed on the major research works that have made a great impact in character recognition. From a methodological point of view we will present the different steps that have been employed in OCR. And finally the most important industrial character recognisers will be covered along with the character data bases that are used in testing the various algorithms.     

文字相关的单字符字体识别方法

汉字具有丰富的字体类型,并且不同的字体在汉字结构上有显著的不同,现在的OCR技术侧重字的识别,而对字体识别的关注较少。提出文字相关的单字符字体识别方法,利用文字相关的先验信息及字体结构特征,对字体的相似性度量采用向量空间模型,并针对常用66款简体字进行实验,得到了较好的平均识别率。     

Offline Chinese handwriting recognition :an assessment of current technology

Offline Chinese handwriting recognition (OCHR) is a typically difficult pattern recognition problem. Many authors have presented various approaches to recognizing its different aspects. We present a survey and an assessment of relevant papers appearing in recent publications of relevant conferences and journals, including those appearing in ICDAR, SDIUT, IWFHR, ICPR, PAMI, PR, PRL, SPIEDRR, and IJDAR. The methods are assessed in the sense that we document their technical approaches, strengths, and weaknesses, as well as the data sets on which they were reportedly tested and on which results were generated. We also identify a list of technology gaps with respect to Chinese handwriting recognition and identify technical approaches that show promise in these areas as well as identify the leading researchers for the applicable topics, discussing difficulties associated with any given approach.     

A genetic sparse distributed memory approach to the application of handwritten character recognition

Kanerva's Sparse Distributed Memory (SDM) is one of the self-organizing neural networks that mimic closely the psychological behavior of the human brain. In this paper, a Genetic Sparse Distributed Memory (GSDM) model that combines SDM with genetic algorithms is proposed. The proposed GSDM model not only maintains the advantages of both SDM and genetic algorithms, but also has higher memory utilization to improve the recognition rate. Its effective performance is also verified by application to Optical Character Recognition (OCR). Experimental results reveal the feasibility and validity of the proposed model.     

深度人脸表情识别研究进展

随着人脸表情识别任务逐渐从实验室受控环境转移至具有挑战性的真实世界环境,在深度学习技术的迅猛发展下,深度神经网络能够学习出具有判别能力的特征,逐渐应用于自动人脸表情识别任务。目前的深度人脸表情识别系统致力于解决以下两个问题：1）由于缺乏足量训练数据导致的过拟合问题;2）真实世界环境下其他与表情无关因素变量（例如光照、头部姿态和身份特征）带来的干扰问题。本文首先对近十年深度人脸表情识别方法的研究现状以及相关人脸表情数据库的发展进行概括。然后,将目前基于深度学习的人脸表情识别方法分为两类：静态人脸表情识别和动态人脸表情识别,并对这两类方法分别进行介绍和综述。针对目前领域内先进的深度表情识别算法,对其在常见表情数据库上的性能进行了对比并详细分析了各类算法的优缺点。最后本文对该领域的未来研究方向和机遇挑战进行了总结和展望：考虑到表情本质上是面部肌肉运动的动态活动,基于动态序列的深度表情识别网络往往能够取得比静态表情识别网络更好的识别效果。此外,结合其他表情模型如面部动作单元模型以及其他多媒体模态,如音频模态和人体生理信息能够将表情识别拓展到更具有实际应用价值的场景。     

Model based odia numeral recognition using fuzzy aggregated features

In this paper, an efficient scheme for recognition of handwritten Odia numerals using hidden markov model （HMM） has been proposed. Three different feature vectors for each of the numeral is generated through a polygonal approximation of object contour. Subsequently, aggregated feature vector for each numeral is derived from these three primary feature vectors using a fuzzy inference system. The final feature vector is divided into three levels and interpreted as three different states for HMM. Ten different three-state ergodic hidden markov models （HMMs） are thus constructed corresponding to ten numeral classes and parameters are calculated from these models. For the recognition of a probe numeral, its log-likelihood against these models are computed to decide its class label. The proposed scheme is implemented on a dataset of 2500 handwritten samples and a recognition accuracy of 96.3% has been achieved. The scheme is compared with other competent schemes.     

Printed Arabic character recognition using HMM

The Arabic Language has a very rich vocabulary. More than 200 million people speak this language as their native speaking, and over 1 billion people use it in several religion-related activities. In this paper a new technique is presented for recognizing printed Arabic characters. After a word is segmented, each character/word is entirely transformed into a feature vector. The features of printed Arabic characters include strokes and bays in various directions, endpoints, intersection points, loops, dots and zigzags. The word skeleton is decomposed into a number of links in orthographic order, and then it is transferred into a sequence of symbols using vector quantization. Single hidden Markov model has been used for recognizing the printed Arabic characters. Experimental results show that the high recognition rate depends on the number of states in each sample.