Abstract—A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and segment(s) of the input word image is used to rank the lexicon entries in order of best match. Variable duration for each character is defined and used during the matching. Experimental results prove that our approach using the variable duration outperforms the method using fixed duration in terms of both accuracy and speed. Speed of the entire recognition process is about 200 msec on a single SPARC-10 platform and the recognition accuracy is 96.8 percent are achieved for lexicon size of 10, on a database of postal words captured at 212 dpi. Index Terms—Handwritten word recognition, segmentation algorithm, variable duration, chain code representation, dynamic

We review the field of offline cursive word recognition. We mainly deal with the various methods that were proposed to realize the core of recognition in a word recognition system. These methods are discussed in view of the two most important properties of such a system: the size and nature of the lexicon involved, and whether or not a segmentation stage is present. We classify the field into three categories: segmentation-free methods, which compare a sequence of observations derived from a word image with similar references of words in the lexicon; segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word; and the perception-oriented approach, that relates to methods that perform a human-like reading technique, in which anchor features found all over the word are used to bootstrap a few candidates for a final evaluation phase.

The performance of any word recognizer depends on the lexicon presented. Usually large lexicons or lexicons containing similar entries pose greater di#culty for recognizers.

In this paper, we propose a new HMM-based approach for off-line signature verification. One of the novelty aspects of our method lies in the ability to dynamically and automatically derive the various author-dependent parameters, required to set an optimal decision rule for the verification process. In this context, the cross-validation principle is used to derive not only the best HMM models, but also an optimal acceptation / rejection decision threshold for each author. This leads to a high discrimination between actual authors and impostors in the context of random forgeries. To quantitatively evaluate the generalization capabilities of our approach, we considered two conceptually different experimental tests carried out on two sets of 40 and 60 authors respectively, each author providing 40 signatures. The results obtained on these two sets show the robustness of our approach.

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This article presents the first results of a system developed to recognize automatically handwritten dates on Brazilian bankchecks.

Prior arts in handwritten word recognition model either discrete features or continuous features but not both. This paper combines discrete symbols and continuous attributes into structural handwriting features and model them by transition-emitting and state-emitting hidden Markov models. The models are rigorously defined and experiments have proved their effectiveness. Markov processes, handwriting analysis. Index Terms I.

This paper introduces a complete system for recognition of Farsi Nastaaligh handwritten words using a continuous-density variable-duration hidden Markov

Abstract: In this study, we introduce a set of one-dimensional features to represent two dimensional shape information for HMM (Hidden Markov Model) based handwritten optical character recognition problem. The proposed feature set embeds two-dimensional information into an observation sequence of one-dimensional string, selected from a code-book. It provides a consistent normalization among distinct classes of shapes, which is very convenient for HMM based shape recognition schemes. The normalization parameters, which maximize the recognition rate, are dynamically estimated in the training stage of HMM. The proposed character recognition system is tested on handwritten data of the NIST database and a local database. The experimental results indicate very high recognition rates.

We describe a system for recognizing unconstrained Turkish handwritten text. Turkish has agglutinative morphology and theoretically an infinite number of words that can be generated by adding more suffixes to the word. This makes lexicon-based recognition approaches, where the most likely word is selected among all the alternatives in a lexicon, unsuitable for Turkish. We describe our approach to the problem using a Turkish prefix recognizer. First results of the system demonstrates the promise of this approach, with top-10 word recognition rate of about 40 % for a small test data of mixed handprint and cursive writing. The lexicon-based approach with a 17,000 word-lexicon (with test words added) achieves 56 % top-10 word recognition rate.