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.     

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.     

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.     

基于SoPC的嵌入式文字识别系统设计

设计了一种基于SoPC的嵌入式文字识别系统。在FPGA平台下,基于SoPC框架搭建软硬件协同系统,设计硬件电路完成文字图像的采集和预处理,嵌入Linux系统,使用其下的识别引擎完成文字图像的识别。采用Altera公司的SoPC builder构建系统框架,Quartus II完成硬件电路的设计,在宿主机Linux环境下完成了软件部分的交叉编译并嵌入到FPGA平台。整体设计在DE2-70开发板上完成了系统验证。     

An intelligent character recognizer for Telugu scripts using multiresolution analysis and associative memory

The present work is an attempt to develop a robust character recognizer for Telugu texts. We aim at designing a recognizer, which exploits the inherent characteristics of the Telugu Script. Our proposed method uses wavelet multi-resolution analysis for the purpose extracting features and associative memory model to accomplish the recognition tasks. Our system learns the style and font from the document itself and then it recognizes the remaining characters in the document. The major contribution of the present study can be outlined as follows. It is a robust OCR system for Telugu printed text. It avoids feature extraction process and it exploits the inherent characteristics of the Telugu character by a clever selection of Wavelet Basis function, which extracts the invariant features of the characters. It has a Hopfield-based Dynamic Neural Network for the purpose of learning and recognition. This is important because it overcomes the inherent difficulties of memory limitation and spurious states in the Hopfield Network. The DNN has been demonstrated to be efficient for associative memory recall. However, though it is normally not suitable for image processing application, the multi-resolution analysis reduces the sizes of the images to make the DNN applicable to the present domain. Our experimental results show extremely promising results.     

Learning stochastic edit distance: Application in handwritten character recognition

Many pattern recognition algorithms are based on the nearest-neighbour search and use the well-known edit distance, for which the primitive edit costs are usually fixed in advance. In this article, we aim at learning an unbiased stochastic edit distance in the form of a finite-state transducer from a corpus of (input, output) pairs of strings. Contrary to the other standard methods, which generally use the Expectation Maximisation algorithm, our algorithm learns a transducer independently on the marginal probability distribution of the input strings. Such an unbiased way to proceed requires to optimise the parameters of a conditional transducer instead of a joint one. We apply our new model in the context of handwritten digit recognition. We show, carrying out a large series of experiments, that it always outperforms the standard edit distance.     

On-line handwritten Chinese character recognition via a fuzzy attribute representation

Chinese characters are constructed by strokes according to structural rules. Therefore, the geometric configurations of characters are important features for character recognition. In handwritten characters, stroke shapes and their spatial relations may vary to some extent. The attribute value of a structural identification is then a fuzzy quantity rather than a binary quantity. Recognizing these facts, we propose a fuzzy attribute representation (FAR) to describe the structural features of handwritten Chinese characters for an on-line Chinese character recognition (OLCCR) system. With a FAR. a fuzzy attribute graph for each handwritten character is created, and the character recognition process is thus transformed into a simple graph matching problem. This character representation and our proposed recognition method allow us to relax the constraints on stroke order and stroke connection. The graph model provides a generalized character representation that can easily incorporate newly added characters into an OLCCR system with an automatic learning capability. The fuzzy representation can describe the degree of structural deformation in handwritten characters. The character matching algorithm is designed to tolerate structural deformations to some extent. Therefore, even input characters with deformations can be recognized correctly once the reference dictionary of the recognition system has been trained using a few representative learning samples. Experimental results are provided to show the effectiveness of the proposed method.