Vuori, V., Adaptive methods for on-line recognition of isolated handwritten characters. ABSTRACT The main goal of the work presented in this thesis has been the development of an on-line handwriting recognition system which is able to recognize handwritten characters of several different writing styles and is able to improve its performance by adapting itself to new writing styles. The recognition method should be applicable to hand-held devices of limited memory and computational resources. The adaptation process should take place during normal use of the device, not in some specific training mode. For the usability aspect of the recognition system, the recognition and adaptation processes should be easily understandable to the users. The first part of this thesis gives an introduction to the handwriting recognition. The topics considered include: the variations present in personal handwriting styles; automatic grouping of similar handwriting styles; the differences between writer-independent and writer-dependent as well as on-line and off-line handwriting recognition problems; the different approaches to on-line handwriting recognition; the previous adaptive recognition systems and the experiments performed with them; the recognition performance requirements and other usability issues related to on-line handwriting recognition; the current trends in on-line handwriting recognition research; the recognition results obtained with the most recent recognition systems; and the commercial applications. The second part of the thesis describes an adaptive on-line character recognition system and the experiments performed with it. The recognition system is based on prototype matching. The comparisons between the character samples and prototypes are based on the Dynamical Time Warping (DTW) algorithm and the input characters are classified according to the k Nearest Neighbors (k-NN) rule. The initial prototype set is formed by clustering character samples collected from a large number of subjects. Thus, the recognition system can handle various writing styles. This thesis work introduces four DTW-based clustering algorithms which can be used for the prototype selection. The recognition system adapts to new writing styles by modifying its prototype set. This work introduces several adaptation strategies which add new writer-dependent prototypes into the initial writer-independent prototype set, reshape the existing prototypes with a Learning Vector Quantization (LVQ)-based algorithm, and inactivate poorly performing prototypes. The adaptations are carried out on-line in a supervised or self-supervised fashion. In the former case, the user explicitly labels the input characters which are used as training samples in the adaptation process. In the latter case, the system deduces the labels from the recognition …

Adaptive methods for on-line recognition of isolated handwritten characters

This work reports experiments with four hierarchical clustering algorithms and two clustering indices for online handwritten character recognition. 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 different clustering algorithms. However, the number of clusters cannot be determined automatically, but some human interventions are required.

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A comparison of techniques for automatic clustering of handwritten characters

Classifier combining is an effective way of improving classification performance. User adaptation is clearly another valid approach for improving performance in a user-dependent system, and even though adaptation is usually performed on the classifier level, also adaptive committees can be very effective. Adaptive committees have the distinct ability of performing adaptation without detailed knowledge of the classifiers. Adaptation can therefore be used even with classification systems that intrinsically are not suited for adaptation, whether that be due to lack of access to the workings of the classifier or simply a classification scheme not suitable for continuous learning. This thesis proposes methods for adaptive combination of classifiers in the setting of on-line handwritten character recognition. The focal part of the work introduces adaptive classifier combination schemes, of which the two most prominent ones are the Dynamically Expanding Context (DEC) committee and the ClassConfidence Critic Combining (CCCC) committee. Both have been shown to be capable of successful adaptation to the user in the task of on-line handwritten character recognition. Particularly the highly modular CCCC framework has shown impressive performance also in a doubly-adaptive setting of combining adaptive classifiers by using an adaptive committee. In support of this main topic of the thesis, some discussion on a methodology for deducing correct character labeling from user actions is presented. Proper labeling is paramount for effective adaptation, and deducing the labels from the user’s actions is necessary to perform adaptation transparently to the user. In that way, the user does not need to give explicit feedback on the correctness of the recognition results. Also, an overview is presented of adaptive classification methods for singleclassifier adaptation in handwritten character recognition developed at the Laboratory of Computer and Information Science of the Helsinki University of Technology, CIS-HCR. Classifiers based on the CIS-HCR system have been used in

http://lib.tkk.fi/Diss/2007/isbn9789512286904/isbn9789512286904.pdf

Adaptive combinations of classifiers with application to on-line handwritten character recognition

A handwritten character recognition system is provided, the system includes a memory module, a signal collection module, a normalization module, a feature extraction module, a general classification module, a special classification module and a display module. Also, a handwritten character recognition method is provided.

Handwritten character recognition method and system

Vector comparison is essential in pattern recognition. Numerous methods based on distance computation are available to carry out such comparison. Unfortunately most of them are applicable only if the vectors are of the same length or do not take into account components misalignment. This paper presents a new distance between two representations called the elastic distance and based on the dynamic programming technique. Properties are studied. We show that it leads to a variant of the least vector quantisation technique that learns the best representants of a group of prototypes. A new centroid computation algorithm is proposed. Finally, the learning scheme algorithm has been successfully applied on an online numerical handwritten character recognition problem using a previously computed centroid of a set of prototypes.

F. Andrianasy

Papers

A new learning scheme for the recognition of dynamical handwritten characters