A scenario model advocating user-driven adaptive document recognition systems

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.