SymbolDesign: A User-centered Method to Design Pen-based Interfaces and Extend the Functionality of Pointer Input Devices

A method called "SymbolDesign" is proposed that can be used to design user-centered interfaces for pen-based input devices. It can also extend the functionality of pointer input devices such as the traditional computer mouse or the Camera Mouse, a camera-based computer interface. Users can create their own interfaces by choosing single-stroke movement patterns that are convenient to draw with the selected input device and by mapping them to a desired set of commands. A pattern could be the trace of a moving finger detected with the Camera Mouse or a symbol drawn with an optical pen. The core of the SymbolDesign system is a dynamically created classifier, in the current implementation an artificial neural network. The architecture of the neural network automatically adjusts according to the complexity of the classification task. In experiments, subjects used the SymbolDesign method to design and test the interfaces they created, for example, to browse the web. The experiments demonstrated good recognition accuracy and responsiveness of the user interfaces. The method provided an easily-designed and easily-used computer input mechanism for people without physical limitations, and, with some modifications, has the potential to become a computer access tool for people with severe paralysis.National Science Foundation (IIS-0093367, IIS-0308213, IIS-0329009, EIA-0202067

A generic approach for desining on-line handwritten shapes recognizers

This paper presents a generic approach for designing on-line handwritten shapes recognizers. Our approach allows designing very different recognition engines that correspond to various needs in pen-based interfaces. In particular, it allows dealing with a wide class of symbols and characters. We present in detail our system and make the link between our models and more standard statistical models such as Hierarchical Hidden Markov Models and Dynamic Bayesian Networks. We then evaluate fundamental properties of our approach: learning from scratch any symbol, learning from very few training sample. We show experimentally that, using our approach, one can learn both a state-of-the-art writerindependent recognizer for alphanumeric characters, and a writer-dependent recognizer working with any twodimensional shapes that learns a new symbol with a few training samples and requires very few machines resources.Dans ce papier, nous présentons une approche générique pour le développement de moteurs de reconnaissance de symboles manuscrits en ligne. Cette approche permet de concevoir des systèmes de reconnaissance de types très variés correspondant à différents contextes des interfaces stylo, pouvant notamment fonctionner sur diverses classes de caractères ou symboles. Nous présentons en détail notre approche et faisons le lien avec d’une part les modèles de Markov hiérarchiques et d’autre part les réseaux bayésiens dynamiques. Nous évaluons ensuite les propriétés fondamentales de notre approche qui lui confèrent une grande flexibilité. Puis nous montrons que l’on peut, avec cette approche générique, concevoir aussi bien des systèmes omni-scripteur rivalisant avec les meilleurs systèmes actuels sur des caractères alphanumériques usuels, que des systèmes mono-scripteur pour des symboles graphiques quelconques, nécessitant très peu d’exemples d’apprentissage et peu gourmands en ressources machine

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject