Use of a priori knowledge for character recognition
G. Houle, Kie Bum Eom · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Research and applications in recognition of machine-printed characters have been active for more than 30 years. It is believed to be a solved problem when the quality of the text is acceptable (minimum fragmented or touching characters). However, when characters are broken or touching, character segmentation still poses a challenge in machine reading. Yet, humans are capable of recognizing very degraded characters with and without context. This paper presents a system for recognizing degraded machine-printed characters. This system relies on a priori knowledge of character shapes. Because classification performance is strongly dependent on the input feature set, this paper focuses on the creation of features from curvature estimation. A set of `clean'' characters from multiple fonts was used to emphasize our belief that a set of clean characters can be used to build an inference engine to recognize noisy characters. The word `noisy'' is used in the generic sense to indicate variations not found in the training set, such as shape variation of new fonts, or broken and touching characters. In addition, we present some concepts on the design of an inference engine that can recognize very degraded images. The inference engine is an assembly of networks (neural and knowledge-based) in which each network stores a flexible representation of a character. The topological features used allow approximate matching for position, direction, curvature, and piecewise incomplete stroke. An image begins to be recognized as soon as some features are detected. Excited networks help to focus attention on uncovering, reinforcing, or ignoring neighboring features. Ultimately, the networks activities are stabilized, and the output consists of a ranked list of possible candidates.