A new statistical approach based on global typographical features is proposed to the widely neglected problem of font recognition. It aims at the identification of the typeface, weight, slope and size of the text from an image block without any knowledge of the content of that text. The recognition is based on a multivariate Bayesian classifier and operates on a given set of known fonts. The effectiveness of the adopted approach has been experimented on a set of 280 fonts. Font recognition accuracies of about 97 percent were reached on high-quality images. In addition, rates higher than 99.9 percent were obtained for weight and slope detection. Experiments have also shown the system robustness to document language and text content and its sensitivity to text length.

Assisted document recognition systems have to integrate automatic recognition, manual edition and incremental learning in a single interactive environment. This paper raises the question of the organization of these three kinds of operations. When an analyzer has the ability to improve with use, there is a tradeoff between the benefits of enhancing the accuracy of automatic analysis, and the additional time spent in interacting for feedback communication. The global cost depends then on the sequence of processed entities, and on the relevance of the learning transactions. Notations are introduced to describe the evolution of a recognition session, and possible organization strategies are discussed. Then a cost model is presented to allow the comparison between different organization schemes. We describe some concrete experiments of cost measures with the ApOFIS font identification tool and the ScanWorX OCR; the first results show that a user-driven approach can potentially save substantial effort in the recognition process, in comparison with machine-driven systems.

We present and discuss a novel approach to modeling logical structures of documents, based on a statistical representation of patterns in a document class. An efficient and error tolerant recognition heuristics adapted to the model is proposed. The statistical approach permits easily automated and incremental learning of the model. The approach has been partially evaluated on a prototype. A discussion of the results achieved by the prototype is finally made.

This paper focuses on the design of a software architecture that can support the programming challenges of CIDRE. This is in itself an important research issue, because in document image analysis as in many other domains, it is a real challenge to integrate coherently all the existing knowledge; that's why the effort should not only be put on inventing new algorithms for specific subtasks, but also on finding a relevant framework to combine various software components. Section 2 addresses the architectural problems raised by the specification of the desired system. Our proposition is presented in section 3, in terms of the integration of existing software components. We show then in section 4 how the proposed framework was successfully used for a restricted application, and what kind of problems are expected in a generalization of the prototype.

A font recognition system allowing the identification of font families, weights, slopes and sizes with an accuracy of 99% for weights and slopes and 96% for families and sizes, has been developed. Our system uses a knowledge base of 240 fonts models, which have been created from a training set of text images written with these different fonts. In this paper, a study of image degradations effects on the system performances, is presented. The evaluation that has been carried out, shows that the system is robust against natural degradations such as those introduced by scanning and photocopying, but its performances decrease with very degraded document images. In order to avoid this weakness, a degradation modeling strategy has been adopted, allowing an automatic adaptation of the system to these degradations. The adaptation is derived from statistical analysis of features behavior against degradations and is performed by specific transformations applied to the system knowledge base. Some promising results are reported.