The automatic and reliable recognition of handwritten documents requires sophisticated and highly adapted algorithms. This includes a complete chain from image analysis, segmentation, and recognition of single characters to a contextual analysis. Many successful commercial systems are based on artificial neural networks as classifiers and have been pushed to their limits. We have realized a prototype system for the semi-automatic recognition of handwritten German bank forms at a high rate.
Character recognition works almost as well on binary images.
We have conceived a completely binary cluster algorithm reducing both memory requirement and computing time by a large factor. Only for difficult cases, an exhaustive search is required. Context information is used to improve the classification result. Optimized Library We have developed a portable ANSI C library for the efficient realization of binary recognition and segmentation.