An online handwriting recognition system for Turkish

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon

A character segmentation algorithm for off-line handwritten script recognition

In this paper, a new character segmentation algorithm for dealing with off-line handwritten script recognition is presented. The X-axis projection, Y-axis projection and geometric classes techniques used by the algorithm proves to be successful in segmenting normal handwriting with a success rate of 93.5%. As a result of this development, detailed understanding of geometric classes of English characters and the difficult cases in segmentation was gained. Although the algorithm works quite well with a randomly chosen sample, results of a detailed analysis may shed new light into the tuning of the algorithm especially for segmenting the identified difficult cases.published_or_final_versio

Improving digital ink interpretation through expected type prediction and dynamic dispatch

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 67-70).Interpretation accuracy of current applications dependent on interpretation of handwritten "digital ink" can be improved by providing contextual information about an ink sample's expected type. This expected type, however, has to be known or provided a priori, and poses several challenges if unknown or ambiguous. We have developed a novel approach that uses a classic machine learning technique to predict this expected type from an ink sample. By extracting many relevant features from the ink, and performing generic dimensionality reduction, we can obtain a minimum prediction accuracy of 89% for experiments involving up to five different expected types. With this approach, we can create a "dynamic dispatch interpreter" by biasing interpretation differently according to the predicted expected types of the ink samples. When evaluated in the domain of introductory computer science, our interpreter achieves high interpretation accuracy (87%), an improvement from Microsoft's default interpreter (62%), and comparable with other previous interpreters (87-89%), which, unlike ours, require additional expected type information for each ink sample.by Kah Seng Tay.M.Eng

Reconnaissance de l'écriture manuscrite en-ligne par approche combinant systèmes à vastes marges et modèles de Markov cachés

Handwriting recognition is one of the leading applications of pattern recognition and machine learning. Despite having some limitations, handwriting recognition systems have been used as an input method of many electronic devices and helps in the automation of many manual tasks requiring processing of handwriting images. In general, a handwriting recognition system comprises three functional components; preprocessing, recognition and post-processing. There have been improvements made within each component in the system. However, to further open the avenues of expanding its applications, specific improvements need to be made in the recognition capability of the system. Hidden Markov Model (HMM) has been the dominant methods of recognition in handwriting recognition in offline and online systems. However, the use of Gaussian observation densities in HMM and representational model for word modeling often does not lead to good classification. Hybrid of Neural Network (NN) and HMM later improves word recognition by taking advantage of NN discriminative property and HMM representational capability. However, the use of NN does not optimize recognition capability as the use of Empirical Risk minimization (ERM) principle in its training leads to poor generalization. In this thesis, we focus on improving the recognition capability of a cursive online handwritten word recognition system by using an emerging method in machine learning, the support vector machine (SVM). We first evaluated SVM in isolated character recognition environment using IRONOFF and UNIPEN character databases. SVM, by its use of principle of structural risk minimization (SRM) have allowed simultaneous optimization of representational and discriminative capability of the character recognizer. We finally demonstrate the various practical issues in using SVM within a hybrid setting with HMM. In addition, we tested the hybrid system on the IRONOFF word database and obtained favourable results.Nos travaux concernent la reconnaissance de l'écriture manuscrite qui est l'un des domaines de prédilection pour la reconnaissance des formes et les algorithmes d'apprentissage. Dans le domaine de l'écriture en-ligne, les applications concernent tous les dispositifs de saisie permettant à un usager de communiquer de façon transparente avec les systèmes d'information. Dans ce cadre, nos travaux apportent une contribution pour proposer une nouvelle architecture de reconnaissance de mots manuscrits sans contrainte de style. Celle-ci se situe dans la famille des approches hybrides locale/globale où le paradigme de la segmentation/reconnaissance va se trouver résolu par la complémentarité d'un système de reconnaissance de type discriminant agissant au niveau caractère et d'un système par approche modèle pour superviser le niveau global. Nos choix se sont portés sur des Séparateurs à Vastes Marges (SVM) pour le classifieur de caractères et sur des algorithmes de programmation dynamique, issus d'une modélisation par Modèles de Markov Cachés (HMM). Cette combinaison SVM/HMM est unique dans le domaine de la reconnaissance de l'écriture manuscrite. Des expérimentations ont été menées, d'abord dans un cadre de reconnaissance de caractères isolés puis sur la base IRONOFF de mots cursifs. Elles ont montré la supériorité des approches SVM par rapport aux solutions à bases de réseaux de neurones à convolutions (Time Delay Neural Network) que nous avions développées précédemment, et leur bon comportement en situation de reconnaissance de mots