On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net

On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples

Shape Matching by Elastic Deformation

Coordinated Science Laboratory was formerly known as Control Systems Laborator

Knowledge integration in a multiple classifier system

This paper introduces a knowledge integration framework based on Dempster-Shafer's mathematical theory of evidence for integrating classification results derived from multiple classifiers. This framework enables us to understand in which situations the classifiers give uncertain responses, to interpret classification evidence, and allows the classifiers to compensate for their individual deficiencies. Under this framework, we developed algorithms to model classification evidence and combine classification evidence form difference classifiers, we derived inference rules from evidential intervals for reasoning about classification results. The algorithms have been implemented and tested. Implementation issues, performance analysis and experimental results are presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44300/1/10489_2004_Article_BF00117809.pd

RECONNAISSANCE DE FORMES APPLIQUEE A L’ECRITURE ARABEMANUSCRITE PAR DES MULTICLASSIFIEURS

Le présent travail porte sur une étude concernant le domaine de reconnaissance de formes appliqué sur l’écriture arabe manuscrite par des multiclassifieurs, D’abords il s’agit de faire une étude générale sur la reconnaissance de formes, puis de faire une étude bibliographique sur les systèmes existants et les différentes recherches effectuées sur ce domaine, ensuite de faire une étude sur les caractéristiques morphologiques et structurelles de l’écriture Arabe, puis étudier les systèmes de classification couramment utilisés, ainsi que des concepts de bases des combinaisons parallèles des classifieurs. Pour enfin proposer un système multiclassifieur de reconnaissance de mots arabes dans un lexique défini

Computer analysis of composite documents with non-uniform background.

The motivation behind most of the applications of off-line text recognition is to convert data from conventional media into electronic media. Such applications are bank cheques, security documents and form processing. In this dissertation a document analysis system is presented to transfer gray level composite documents with complex backgrounds and poor illumination into electronic format that is suitable for efficient storage, retrieval and interpretation. The preprocessing stage for the document analysis system requires the conversion of a paper-based document to a digital bit-map representation after optical scanning followed by techniques of thresholding, skew detection, page segmentation and Optical Character Recognition (OCR). The system as a whole operates in a pipeline fashion where each stage or process passes its output to the next stage. The success of each stage guarantees that the operation of the system as a whole with no failures that may reduce the character recognition rate. By designing this document analysis system a new local bi-level threshold selection technique was developed for gray level composite document images with non-uniform background. The algorithm uses statistical and textural feature measures to obtain a feature vector for each pixel from a window of size (2 n + 1) x (2n + 1), where n ≥ 1. These features provide a local understanding of pixels from their neighbourhoods making it easier to classify each pixel into its proper class. A Multi-Layer Perceptron Neural Network is then used to classify each pixel value in the image. The results of thresholding are then passed to the block segmentation stage. The block segmentation technique developed is a feature-based method that uses a Neural Network classifier to automatically segment and classify the image contents into text and halftone images. Finally, the text blocks are passed into a Character Recognition (CR) system to transfer characters into an editable text format and the recognition results were compared to those obtained from a commercial OCR. The OCR system implemented uses pixel distribution as features extracted from different zones of the characters. A correlation classifier is used to recognize the characters. For the application of cheque processing, this system was used to read the special numerals of the optical barcode found in bank cheques. The OCR system uses a fuzzy descriptive feature extraction method with a correlation classifier to recognize these special numerals, which identify the bank institute and provides personal information about the account holder. The new local thresholding scheme was tested on a variety of composite document images with complex backgrounds. The results were very good compared to the results from commercial OCR software. This proposed thresholding technique is not limited to a specific application. It can be used on a variety of document images with complex backgrounds and can be implemented in any document analysis system provided that sufficient training is performed.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .A445. Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1061. Advisers: Maher Sid-Ahmed; Majid Ahmadi. Thesis (Ph.D.)--University of Windsor (Canada), 2004

Real-time hand printed character recognition on a DSP chip

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 119-120).by Christopher Isaac Chang.M.S

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

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

Extracção automática de dados georreferenciados a partir dos planos cadastrais portugueses

Tese dout., Engenharia Electrónica e Computação, Universidade do Algarve, 2009Image recognition algorithms are used to extract information from digitized images automatically. Systems designed to convert paper documents into meaningful vectorial representations are numerous nowadays, and have been constantly improved over the two last decades. However, none of these systems seems to be able to provide satisfying results when it comes to convert complex documents such as technical drawings, usually semantic of the problem is not considered and post-processing costs remain high. This dissertation presents a set of techniques that greatly simplifies the automatic extraction of cadastral entities from the portuguese cadastral maps. The validity of the approach is illustrated designing a prototype system, joining all recognition algorithms and validating all information.Fundação para a Ciência e Tecnologia (FCT