Toru Wakahara

The state of the art in online handwriting recognition

This survey describes the state of the art of online handwriting recognition during a period of renewed activity in the field. It is based on an extensive review of the literature, including journal articles, conference proceedings, and patents. Online versus offline recognition, digitizer technology, and handwriting properties and recognition problems are discussed. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined. >

On-line handwriting recognition

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. >

Shape matching using LAT and its application to handwritten numeral recognition

This paper describes an iterative technique for gradually deforming a mask binary image with successive local affine transformation (LAT) operations so as to yield the best match to an input binary image as one new and promising approach toward robust handwritten character recognition. The method uses local shapes in the sense that the LAT of each point at one location is optimized using locations of other points by means of least-squares data fitting using Gaussian window functions. It also uses a multiscale refinement technique that decreases the spread of window functions with each iteration. Especially in handwritten character recognition, structural information is indispensable for robust shape matching or discrimination. The method is enhanced to explicitly incorporate structures by weighting the above least-squares criterion with similarity measures of both topological and geometric features of the mask and input images. Moreover, deformation constraints are imposed on each iteration, not only to promote and stabilize matching convergence but also to suppress an excessive matching process. Shape matching experiments have been successfully carried out using skeletons of totally unconstrained handwritten numerals. >

State of the art of handwritten numeral recognition in Japan-The results of the first IPTP character recognition competition

The institute for Posts and Telecommunications Policy (IPTP) held its first character recognition competition in 1992 to ascertain the present status on ongoing research in character recognition and to find promising algorithms for handwritten numerals. The authors report the results of this competition and attempt to analyze substituted or rejected patterns in an effort to demonstrate the limitations of the present recognition technology and to clarify the remaining problems. They ascertain that by combining the complementary characteristics of each into a recognition system, the recognition ability is assumed to be remarkedly improved. Furthermore, they discuss a variety of combination rules for the multiexpert system and present promising results.<<ETX>>

Online handwriting recognition-a survey

The state of the art of online handwriting recognition is surveyed based on an extensive review of the literature. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined. It is found that online recognizers, except for bar-code readers and other specialized equipment, are not applicable to machine-printed characters.<<ETX>>

On-line cursive Kanji character recognition as stroke correspondence problem

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.

On-line cursive Kanji character recognition using stroke-based affine transformation

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.

Adaptive normalization of handwritten characters using global/local affine transformation

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.

Dot image matching using local affine transformation

The author describes an iterative technique for gradually deforming a mask dot image with successive local affine transformation (LAT) operations so as to yield the best match to an nput dot image. The method uses local structure in the sense that the LAT of a dot at one location is optimized using locations of other dots by means of least-squares data fitting with Gaussian window functions. A multiscale or coarse-fine strategy has been implemented in the algorithm by decreasing the effective interacting neighborhood with each iteration. This technique successfully extracted distortions or displacements from dot images of handwritten figures and random-dot stereograms.<<ETX>>

An iterative image registration technique using local affine transformation

For registration between images, algorithms of point correspondence searching type and deformation field analyzing type have been proposed in the past. The former had a problem of increasing processing quantity and the latter had a problem of assuming small displacement or deformation. This paper proposes a cooperative image registration technique which absorbs finite displacement and nonrigid body deformation by iteratively applying affine transformations from coarse to fine for binary images expressed by sets of loci vectors of characteristic points. The process consists of four steps: (1) for each characteristic point of a reference image, a local affine transformation (LAT) weighted by Gaussian window functions is applied; (2) based on the least-squares method, LAT is optimized for each characteristic point of the reference image so that overlapping for a group of characteristic points of input images becomes best; (3) optimized LAT is applied for each characteristic point of the reference image and deformed reference image is generated; (4) iterating steps (1) through (3) while gradually decreasing the spreads of the Gaussian window functions, when the deformed image coincides with the input image, displacement between images is determined. We have applied this method to binary character images including large deformation and random-dot stereograms and obtained good results. Also, we evaluated the processing quantity by this method and showed that extension to gray scale images is easily formulated.