Recognition system for unconstrained handwritten numerals

In this paper, we present a recognition system of unconstrained handwritten numerals . We describe all essential stages to it s elaboration . We approach the first phase of all recognition system : the extraction of the primitives . A structure that use th e skeleton of the numeral is used to extract rapidly 55 binary primitives . We specify a method that allows to determine the transmitted information about the primitives on the problem of the recognition of unconstrained handwritten numerals . Information transmitted by each primitive providing a criterion allowing to generate a binary decision tree . This criterion is used to select in each nod e the best primitive . The obtained classifier does not use the totality of 55 binary primitives but solely those that have been retaine d during the phase of identification of the decision tree . We present an original reject criterion that allows to increase performances of the recognition system . Finally, We describe the database of American handwritting numerals that serves to test the classifier . We demonstrate the performance of our system with this database .Nous présentons dans cet article un système de reconnaissance de chiffres manuscrits hors lignes, en décrivant toutes les étapes essentielles à son élaboration. Nous abordons d'abord la première phase de tout système de reconnaissance: l'extraction de primitives. Une représentation structurée construite à partir du squelette du chiffre est utilisée pour extraire rapidement un jeu de 55 primitives binaires. Nous précisons ensuite une méthode qui permet de déterminer l'information transmise par une primitive sur le problème de la reconnaissance des chiffres manuscrits hors lignes. L'information transmise par chaque primitive fournit un critère permettant de générer un arbre de décision binaire de manière complètement automatique. Ce critère est utilisé pour sélectionner au niveau de chaque noeud de l'arbre la primitive la plus informative sur le problème de reconnaissance associé au noeud en cours de traitement. Le classifieur obtenu n'utilise pas la totalité des 55 primitives binaires mais uniquement celles qui ont été retenues durant la phase d'identification de l'arbre de décision. Nous présentons ensuite un critère de rejet original qui permet d'augmenter les performances du système de reconnaissance de manière significative. Nous décrivons finalement la base de données de chiffres manuscrits américains qui sert à tester le classifieur. Nous donnons les résultats obtenus

The effectiveness of features in pattern recognition

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Recognition of off-line handwritten cursive text

The author presents novel algorithms to design unconstrained handwriting recognition systems organized in three parts: In Part One, novel algorithms are presented for processing of Arabic text prior to recognition. Algorithms are described to convert a thinned image of a stroke to a straight line approximation. Novel heuristic algorithms and novel theorems are presented to determine start and end vertices of an off-line image of a stroke. A straight line approximation of an off-line stroke is converted to a one-dimensional representation by a novel algorithm which aims to recover the original sequence of writing. The resulting ordering of the stroke segments is a suitable preprocessed representation for subsequent handwriting recognition algorithms as it helps to segment the stroke. The algorithm was tested against one data set of isolated handwritten characters and another data set of cursive handwriting, each provided by 20 subjects, and has been 91.9% and 91.8% successful for these two data sets, respectively. In Part Two, an entirely novel fuzzy set-sequential machine character recognition system is presented. Fuzzy sequential machines are defined to work as recognizers of handwritten strokes. An algorithm to obtain a deterministic fuzzy sequential machine from a stroke representation, that is capable of recognizing that stroke and its variants, is presented. An algorithm is developed to merge two fuzzy machines into one machine. The learning algorithm is a combination of many described algorithms. The system was tested against isolated handwritten characters provided by 20 subjects resulting in 95.8% recognition rate which is encouraging and shows that the system is highly flexible in dealing with shape and size variations. In Part Three, also an entirely novel text recognition system, capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented. This system is an extension of the above recognition system. Tokens are extracted from a onedimensional representation of a stroke. Fuzzy sequential machines are defined to work as recognizers of tokens. It is shown how to obtain a deterministic fuzzy sequential machine from a token representation that is capable'of recognizing that token and its variants. An algorithm for token learning is presented. The tokens of a stroke are re-combined to meaningful strings of tokens. Algorithms to recognize and learn token strings are described. The. recognition stage uses algorithms of the learning stage. The process of extracting the best set of basic shapes which represent the best set of token strings that constitute an unknown stroke is described. A method is developed to extract lines from pages of handwritten text, arrange main strokes of extracted lines in the same order as they were written, and present secondary strokes to main strokes. Presented secondary strokes are combined with basic shapes to obtain the final characters by formulating and solving assignment problems for this purpose. Some secondary strokes which remain unassigned are individually manipulated. The system was tested against the handwritings of 20 subjects yielding overall subword and character recognition rates of 55.4% and 51.1%, respectively