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

Use of many classifiers for multifont text recognition

We present in this paper a character recognition system using many classifiers . Each classifier gives an answer and the final result is selected by majority-vote . The system uses six classifiers built around first and second order hidden Markov models (HMM) as well as nearest neighbor considerations . The majority-vote is chosen so as to give becter results than each of the other systems applied individually. The recognition process is followed by a post-processing which employs combinations ofstochastic and dictionary verification methods forword recognition and error-correction .Nous présentons dans cet article un système de reconnaissance de caractères multifontes utilisant plusieurs classifieurs. Chaque classifieur fournit une réponse puis le résultat final est obtenu par vote majoritaire. Les classifieurs sont de deux types: stochastique et plus proche voisin. Les classifieurs stochastiques sont des modèles de Markov cachés du premier et du second ordre. La reconnaissance des caractères est suivie d'un module de vérification lexicale qui utilise un modèle de Markov caché pour les mots dont les paramètres sont déterminés à partir de statistiques sur la langue et d'un dictionnair

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.

A step towards understanding paper documents

This report focuses on analysis steps necessary for a paper document processing. It is divided in three major parts: a document image preprocessing, a knowledge-based geometric classification of the image, and a expectation-driven text recognition. It first illustrates the several low level image processing procedures providing the physical document structure of a scanned document image. Furthermore, it describes a knowledge-based approach, developed for the identification of logical objects (e.g., sender or the footnote of a letter) in a document image. The logical identifiers provide a context-restricted consideration of the containing text. While using specific logical dictionaries, a expectation-driven text recognition is possible to identify text parts of specific interest. The system has been implemented for the analysis of single-sided business letters in Common Lisp on a SUN 3/60 Workstation. It is running for a large population of different letters. The report also illustrates and discusses examples of typical results obtained by the system

The direction of technical change in AI and the trajectory effects of government funding

Government funding of innovation can have a significant impact not only on the rate of technical change, but also on its direction. In this paper, we examine the role that government grants and government departments played in the development of artificial intelligence (AI), an emergent general purpose technology with the potential to revolutionize many aspects of the economy and society. We analyze all AI patents filed at the US Patent and Trademark Office and develop network measures that capture each patent’s influence on all possible sequences of follow-on innovation. By identifying the effect of patents on technological trajectories, we are able to account for the long-term cumulative impact of new knowledge that is not captured by standard patent citation measures. We show that patents funded by government grants, but above all patents filed by federal agencies and state departments, profoundly influenced the development of AI. These long-term effects were especially significant in early phases, and weakened over time as private incentives took over. These results are robust to alternative specifications and controlling for endogeneity

An algebraic technique for the automatic recognition of visual patterns

Imperial Users onl

Optical image scanners and character recognition devices : a survey and new taxonomy

Includes bibliographical references (p. [54]-[56]).Amar Gupta ... [et al.]