The effectiveness of features in pattern recognition

Imperial Users onl

Feature Extraction Methods for Character Recognition

Not Include

Detection on Straight Line Problem in Triangle Geometry Features for Digit Recognition

Geometric object especially triangle geometry has been widely used in digit recognition area. The triangle geometry properties have been implemented as the triangle features which are used to construct the triangle shape. Triangle is formed based on three points of triangle corner A, B and C. However, a problem occurs when three points of triangle corner were in parallel line. Thus, an algorithm has been proposed in order to solve the straight line problem. The Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) were used to measure based on the classification accuracy. Four datasets were used: HODA, IFCHDB, MNIST and BANGLA. The comparison results classification demonstrated the effectiveness of our proposed method

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules

Offline Handwritten Digit Recognition Using Triangle Geometry Properties

Offline digit handwritten recognition is one of the frequent studies that is being explored nowadays.Most of the digit characters have their own handwriting nature. Recognizing their patterns and types is a challenging task to do.Lately,triangle geometry nature has been adapted to identify the pattern and type of digit handwriting.However,a huge size of generated triangle features and data has caused slow performances and longer processing time.Therefore,in this paper,we proposed an improvement on triangle features by combining the ratio and gradient features respectively in order to overcome the problem.There are four types of datasets used in the experiment which are IFCHDB,HODA,MNIST and BANGLA.In this experiment,the comparison was made based on the training time for each dataset Besides,Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) techniques are used to measure the accuracies for each of datasets in this study

NBS monograph

From Introduction: "This report is the first of a series intended to provide a selective overview of research and development efforts and requirements in the somewhat overlapping fields of the computer and information sciences and technologies. The projected series of reports will attempt to outline the probable range of R & D activities in the computer and information sciences and technologies through selective reviews of the literature and to develop a reasonable consensus with respect to the opinions of workers in these and potentially related fields as to areas of continuing R & D concern for research program planning or review in these areas.

Un nouvel algorithme de sélection de caractéristiques : application à la lecture automatique de l'écriture manuscrite

La problématique abordée dans cette thèse est celle de la reconnaissance de l'écriture manuscrite hors-ligne, avec pour application industrielle le tri automatique du courrier. En effet le Service de Recherche Technique de La Poste (France) nous a donné pour mandat d'améliorer son système de reconnaissance de l'écriture manuscrite. Une analyse approfondie du système existant a permis de dégager une direction principale de recherche: l'amélioration de la représentation de l'information fournie au système de reconnaissance. Elle est caractérisée par deux ensembles finis de primitives, qui sont comnbinés avant intégration dans le système, au moyen d'un produit cartésien. L'amélioration de la représentation de l'information passe par l'extraction de nouvelles primitives. Dans cette optique, trois nouveaux espaces de représentation ont été développés. L'utilisation d'un algorithme de quantification vectorielle permet de construire plusieurs ensembles de primitives. Afin d'augmenter le pouvoir discriminant de ces dernières, différentes stratégies ont été évaluées: l'analyse discriminante linéaire, la technique de zoning et en association avec cette dernière stratégie de pondération des zones. La combinaison des espaces de représentation et des stratégies d'amélioration a conduit à la construction de plusieurs systèmes de reconnaissance obtenant de meilleures performances que système de base. La technique permettant de combiner les ensembles de primitives dans le système de base ne peut pas être utilisée. Un nouvel algorithme a été développé afin d'intégrer de nouveaux ensembles de primitives. L'idée de base est de remplacer les primitives les moins discriminantes d'un ensemble de départ par de nouvelles. Une stratégie effectuant des regroupements de primitives non-discriminantes permet de décomposer la tâche globale de reconnaissance en sous-problèmes. La définition et la sélection dynamique de nouvelles primitives est alors orientée par cette décomposition. L'application de l'algorithme aboutit à une représentation de l'information améliorée caractérisée par une hiérarchie de primitives. Son déroulement automatique permet une adaptation rapide à de nouvelles données ou à la disponibilité d'un nouvel espace de représentation. Les performances du système de base, utilisant la combinaison de deux ensembles de primitives est de 89,5% lors de l'utilisation d'un lexique de taille 1 000. L'amélioration d'un des deux ensembles conduit à une performance de 94,3%, tout en diminuant de 20% le nombre de primitives utilisées