Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

This work shows how a Self-Organizing Map (SOM) can be applied in the analysis of different handwriting styles. The analyzed handwriting samples have been collected in on-line fashion with special writing equipments such as pressure sensitive tablets. The handwriting style of an individual subject is represented by a vector, components of which reflect the tendencies of the writer to use certain prototypical styles for isolated alphanumeric characters. This study shows that correlations between different writing styles, both character-wise and writer-wise can be found. Clusters of different personal writing styles can be found by studying the U-matrix viasualization of the SOM trained with data collected from over 700 subjects. An examination of the component planes of the SOM reveals some interesting correlations between the prototypical character styles. 1.

This work reports experiments with four hierarchical clustering algorithms and two clustering indices for online handwritten characters. The main motivation of the work is to develop an automatic method for finding a set of prototypical characters which would represent well the different writing styles present in a large international database. One of the major obstacles in achieving this goal is the uneven representation of different writing styles in the database. On the basis of the results of the experiments, we claim that a good set of prototypes can be formed from the combined results of the different clustering algorithms. However, the number of clusters cannot be determined automatically but some human intervention is required.

There are two main approaches... In the experiments of this paper the feasibility of using an adaptive committee classifier is explored and tested with on-line character recognition. The results clearly show that the use of adaptive committees can improve on the recognition results, both in comparison to the individual member classifiers and the non-adaptive reference committee.

Adaptation is an effective method for improving classification accuracy and a committee structure can in general improve on its members' performance. Therefore an adaptive committee structure is a tempting approach. Rejection may be used in handwriting recognition to improve performance through either directing the problematic character to a special classifier that handles such hard cases or discarding it. The experiments in this paper compare several fundamentally different approaches to implementing rejection in an adaptive committee classifier. A Dynamically Expanding Context (DEC)-based committee is used for evaluating these approaches. The results show that if the rejected classes are handled with a 50% error rate, the performance is improved. A scheme in which there is an adjustable threshold for distance-based rejection is an effective method for implementing rejection in this setting.

Arabic cursive optical character recognition (OCR) based on the dynamic time warping (DTW) algorithm provides simultaneously very interesting segmentation and recognition rates. However, the computing complexity of the DTW algorithm restricts its widespread utilization and its consideration at a commercial scale. Accelerating the DTW execution time has attracted many researchers and several solutions have already been proposed. These solutions are commonly based on very specialized processors and hardware architectures and as such they remain very expensive and not amenable to a large scale utilization. In a previous work, we found that loosely coupled architectures can indeed provide viable infrastructures to implement a distributed Arabic OCR. Our objective here is to allow the recognition of huge quantities of Arabic documents such as those of certain national libraries. Undoubtedly, enough processing power and storage capabilities are needed. In this paper, we proposed and used a peer-to-peer (P2P) architecture using the scientific research Tunisian grid (SRTG). Conducted experiments testify that our proposed architecture provides very adequate speedups of the DTWbased

This paper discusses a combination of two techniques for improving the recognition accuracy of on-line handwritten character recognition: committee classification and adaptation to the user. A novel adaptive committee structure, namely the Class-Confidence Critic Combination (CCCC) scheme, is presented and evaluated. It is shown to be able to improve significantly on its member classifiers. Also the effect of having either more or less diverse sets of member classifiers is considered.

Abstract In this article we approach neural networks as computational templates that travel across various sciences. Traditionally, it has been thought that models are primarily models of some target systems: they are assumed to represent partially or completely their target systems. We argue, instead, that many computational models cannot easily be conceived of in representational terms. Rather, they can be seen as models for various epistemic endeavors. Apart from dealing with the question of representation, we discuss also what implications the genuinely cross-disciplinary computational templates such as neural networks have for the organization of science. We use Self-organizing maps as an example through which we study the aforementioned questions. I.

Abstract: In spite of the diversity of printed Arabic optical character recognition products and proposals, the problem seems to be not yet well solved. The complex morphology and calligraphy of the Arabic writing on one hand and the use of some light approaches on the other hand are behind the poorness of these products. However, some strong proposed approaches didn’t find the opportunity to be commercialised because of generally their corresponding complex computing. The dynamic time warping algorithm is considered as one among these strong approaches. In fact, several studies and experiments have shown and confirmed that the printed Arabic optical character recognition based on dynamic time warping algorithm provides a very interesting recognition rate especially for large and huge vocabularies. One of the attractive sides of the dynamic time warping algorithm is its ability to recognize properly connected or cursive characters (words or sub words) without prior segmentation. Furthermore, this algorithm performs the recognition process from within a reference library of isolated characters and owns a very good immunity against noises. Unfortunately, the big amount of its computing during the recognition process makes its execution time very slow and, hence, restricts its utilization. Many researchers attempted to speedup the execution time of this algorithm. Unfortunately, the corresponding proposed solutions require generally specific high cost architectures. Loosely coupled architectures such as grapes or grid computing can provide enough power without additional cost to distribute the complexity of some greedy applications. Consequently, we report in this paper the performance analysis of an analytical and an experimental study of a distributed Arabic optical character recognition based

handwriting recognition, prototype learning, clustering In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.