This paper introduces an adaptive or category-dependent normalization method that normalizes an input pattern against each reference pattern using global/local affine transformation (GAT/LAT) in a hierarchical manner as a general deformation model. Also, the normalization criterion is clearly defined as minimization of the mean of nearest-neighbor interpoint distances between each reference pattern and a normalized input pattern. Optimal GAT/LAT is determined by iterative application of weighted least-squares fitting techniques. Experiments using input patterns of 3,171 character categories, including Kanji, Kana, and alphanumerics, written by 36 people in the cursive style against square-style reference patterns show that the proposed method not only can absorb a fairly large amount of handwriting fluctuation within the same category, but the discrimination ability is greatly improved by the suppression of excessive normalization against similarly shaped but different categories. Furthermore, comparative results obtained by the conventional shape normalization method for preprocessing are presented.

We present a novel approach for processing form documents based on background region analysis. Our goal is to achieve line-property-free form processing. Background regions can be extracted independently of line width or length, and multi-layer analysis employing a series of coarse-to-fine background images makes it possible to extract background regions regardless of small line-breaks. We propose two multi-layer analysis algorithms for different situations. One is applied in a registration process of a form model. It reliably extracts box regions from un-filled forms without using any model. The other is applied in a character extraction process. By using a spatial model of a form, it reliably extracts background regions, and re-integrates these regions if they are divided by characters written in the boxes. From these re-integrated regions, the exact locations of the character boxes are determined on the input image. Besides these algorithms, we present a form identification method that uses coarse background images. We implemented the algorithms into a prototype system that processes pre-printed forms. 50 types of existing forms were tested without any customization. Model registration, character extraction, and form identification were reliably carried out.

Conventional normalization methods for handwritten characters have limitations, such as preprocessing operations because they are category-independent. The paper introduces an adaptive or category-dependent normalization method that normalizes an input pattern against each reference pattern using global/local affine transformation (GAT/LAT) in a hierarchical manner as a general deformation model. Experiments using input patterns of 3171 character categories, including Kanji, Kana, and alphanumerics, written by 36 people in the cursive style against square style reference patterns show not only that the proposed method can absorb a fair large amount of handwriting fluctuation within the same category, but also that discrimination ability is greatly improved by the suppression of excessive normalization against similarly shaped but different categories.

We present a distortion-tolerant online cursive Kanji character recognition method that absorbs the stroke-based handwriting distortion expressible by uniform affine transformation. Experiments are made using two kinds of test data in the square style and in the cursive style for 2,980 Kanji character categories; recognition rates of 98.4 percent and 96.0 percent are obtained.

A prototype system for drawing interpretation that automatically converts hand-sketched floor-plans into the CAD format is presented. The main problems in interpreting hand-sketched drawings are positional ambiguity and shape distortion. In the proposed method, positional ambiguity is able to be corrected in a nearest grid-location manner because floor-plan elements are normally drawn on/along grid lines that correspond to the modular proportions. Shape distortion is able to be corrected by template-matching of closed regions because architectural elements mostly consist of one or more closed regions of basic figures: triangles, squares, circles, and so on. The system was tested using 150 floor-plan drawings hand-sketched by three subjects without the use of rulers or templates. The results showed that sufficient recognition performance for practical use was able to be obtained.

This paper describes a distortion-tolerant online Kanji character recognition method using stroke-based affine transformation (SAT). The first part of the method determines one-to-one stroke correspondence between an input pattern and each reference pattern. The second part applies optimal SAT to each stroke of the input pattern to absorb handwriting distortion. The last part calculates the inter-pattern distance between the reference pattern and the SAT-superimposed input pattern. Only a single reference pattern for each of 2,980 Kanji character categories is generated by using training data written carefully with the correct stroke-number and stroke-order. Recognition tests are made using two kinds of test data in the square style and in the cursive style written by 36 different people; recognition rates of 98.4% and 96.0% are obtained.

This paper describes a stroke-number and stroke-order free on-line Kanji character recognition method by a joint use of two complementary algorithms of optimal stroke correspondence determination: one dissolves excessive mapping and the other dissolves deficient mapping. Also, three kinds of inter-stroke distances are devised to deal with stroke concatenation or splitting and heavy shape distortion. Only a single reference pattern for each of 2,980 Kanji character categories is generated by using training data composed of 120 patterns written with the correct stroke-number and stroke-order. Recognition tests are made using the training data and two kinds of resting data in the square style and in the cursive style written by 36 different people; recognition rates of 99.5%, 97.6%, and 94.1% are obtained.

For large-alphabet languages, like Japanese, handwriting input using an online recognition technique is essential for input accuracy and speed. However, there are serious problems that prevent high recognition accuracy of unconstrained handwriting. First, the thousands of ideographic Japanese characters of Chinese origin (called Kanji) can be written with wide variations in the number and order of strokes and significant shape distortions. Also, writing box-free recognition of characters is required to create a better man-machine interface. Intense research performed over the past 15 years to answer the most pressing recognition problems is described. Prototype systems are also described. The man-machine interfaces made possible by online handwriting recognition and anticipated advances in both hardware and software are discussed.>