A Syntactic Omni-Font Character Recognition System

The author introduces a syntactic omni-font character recognition system that recognizes a wide range of fonts, including handprinted characters. A structural pattern-matching approach is used. Essentially, a set of loosely constrained rules specify pattern components and their interrelationships. The robustness of the system is derived from the orthogonal set of pattern descriptors, location functions, and the manner in which they are combined to exploit the topological structure of characters. By virtue of the new pattern description language, PDL, the user may easily write rules to define new patterns for the system to recognize. The system also features scale-invariance and user-definable sensitivity to tilt orientation. The system has achieved a 95. 2% recognition rate

Généralisation de la notion de langage à contexte libre. Application à l'analyse syntaxique de figures

National audienceno abstrac

Architecture and automatized methods : criticisms on the current issues

Thesis. 1975. M.Arch.A.S.--Massachusetts Institute of Technology. Dept. of Architecture.Bibliography: leaves 101-144.by Anne Marie Fourcade.M.Arch.A.S

A syntactic method of weather pattern recognition.

Thesis (M.Phil.)--Chinese University of Hong Kong.Bibliography: leaves 122-126

Generierung interaktiver Lerneinheiten aus visuellen Spezifikationen

In dieser Arbeit wurde der neue Ansatz VIDEA für die Lehre von Algorithmen und Datenstrukturen vorgestellt. VIDEA verbindet diejenigen Eigenschaften multimedialer Lehrwerkzeuge, die nach heutigem Kenntnisstand einen maximalen Lerneffekt ermöglichen, in den Bereichen Visualisierung, Interaktivität und aktivem Lernen auf konsequente und neuartige Weise. Um die Probleme ähnlich zielführender, bestehender Ansätze zu vermeiden, die oft auf einen kleinen Bereich von Datenstrukturen festgelegt sind oder dem nutzenden Dozenten unnötigen Mehraufwand bei der Erstellung oder Anpassung neuer Lerneinheiten verursachen, wurde in unserem Ansatz eine zweistufige Parametrisierbarkeit des Systems entworfen: Auf der ersten Stufe werden zu lerndene Datenstrukturen und Algorithmen inklusive Operationen visuell spezifiziert. Aus dieser Spezifikation wird in einem automatisierten Verfahren eine Lerneinheit generiert, die bereits ablauffähig ist und deren Verhalten auf der zweiten Stufe durch Konfiguration weiter konkretisiert wird. Auf diese Weise können durch Anpassung existierender Spezifikationen schnell neue Lerneinheiten mit den in VIDEA garantierten Eigenschaften zur Erreichung eines hohen Lerneffekts erstellt werden. Ebenso können durch Konfiguration einer bestehenden Lerneinheit weitere Varianten dieser Lerneinheit in anderen Lernszenarien genutzt werden

Design and evaluation of a shape retrieval system

PhD ThesisWhile automated storage and retrieval systems for textual and numeric data are now commonplace, the development of analogous systems for pictorial data has lagged behind - not through the lack of need for such systems, but because their development involves a number of significant problems. The aim of this project is to investigate these problems by designing and evaluating an information retrieval system for a specific class of picture, 2-dimensional engineering drawings. This involves consideration of the retrieval capabilities needed by such· a system, what storage structures it would require, how the salient features of each drawing should be represented, how query and stored shapes should be matched, what features were of greatest importance in retrieval, and the interfaces necessary to formulate queries and display results. A form of hierarchical boundary representation has been devised for stored shapes, in which each boundary can be viewed as a series of levels of steadily increasing complexity. A set of rules for boundary and segment ordering ensures that as far as possible, each shape has a unique representation. For each level at which each boundary can be viewed, a set of invariant shape features characterizing that level is extracted and added to the shape representation stored in the database. Two classes of boundary feature have been defmed; global features, characteristic of the boundary as a whole, and local features, corresponding to individual fragments of the boundary. To complete the shape description, position features are also computed and stored, to specify the pattern of inner boundaries within the overall shape. Six different tYPes of shape retrieval have been distinguished, although the prototype system can offer only three of these - exact shape matching, partial shape matching and similarity matching. Complete or incomplete query shapes can be built up at a terminal, and subjected to a feature extraction process similar to that for stored drawings, yielding a query fIle that can be matched against the shape database. A variety of matching techniques is provided, including similarity estimation using global or local features, tests for the existence of specified local features in stored drawings, and cumulative angle vs distance matching between query and stored shape boundaries. Results can be displayed in text or graphical form. The retrieval performance of the system in similarity matching mode has been evaluated by comparing its rankings of shapes retrieved in response to test queries with those obtained by a group of human subjects faced with the same task. Results, expressed as normalized recall and precision, are encouraging, particularly for similarity estimation using either global or local boundary features. While the detailed results are of limited significance until confrrmed with larger test collections, they appear sufficiently promising to warrant the development of a more advanced prototype capable of handling 3-D geometric models. Some design aspects of the system would appear to be applicable to a wider range of pictorial information systems

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