Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

Despite all the attempts of modern information technologyto supplant paper by sophisticated electronic devices,paper is still omnipresent in virtually any office environment.We have learned over the last decade that paper isan indispensable part of our daily work and that even thelatest computers are not able to mimic its functionality. Today,paper and computer are coexisting side by side withoutinteracting with each other, both having their specificadvantages. This paper presents a concept for bridging thegap between computer and paper by combining the advantagesof both electronic and paper world. The implementationdescribed in this paper augments standard paper withfeatures of modern information technology. Moreover, it allowsinterfacing computers using pen and paper.

This paper presents the UP-Kuchibue and UP-Nakayosi databases containing on-line handwritten Japanese characters. These databases are the international versions of two databases, Kuchibue and Nakayosi, collected in the Nakagawa Laboratory at the University of Agriculture & Technology in Tokyo. They contain more than 3 million characters written by 283 Japanese writers. UP-Kuchibue and UP-Nakayosi are stored in the common Unipen format. Unipen is a western plain ASCII format which allows easy access for international researchers and facilitates international benchmarks.

The paper proposes a model for tracking handwriting based on curvature minimization. Aiming at recovering stroke sequences from words written in the past, the problem is formulated as a graph theoretical question. The solution does not require mathematical functions. In particular loops are considered elementary basic strokes and are not approximated with functions. Recovering stroke sequences is equivalent to ordering the edges of a graph, which can be derived in a straightforward manner from the scanned binary word image. The paper does not deal with any special recognition method. However, an important aim is to provide a mechanism for deriving temporal information to help to improve off-line recognition methods. The most important advantages of this method over methods proposed so far are: a single global principle (global optimization of curvature), implicit modeling of retraced strokes and simplicity.

Off-line handwriting recognition is constrained by the lack of time information. This paper describes an approach to derive sequence information from a written word. The underlying representation of the word used is a graph with nodes and edges resulting from a skeletonization process. The writing path is reconstructed by assuming that the writer has used a path with least curvature, i.e. reconstruction is realized as a global optimization process, which consists of solving a travelling salesman problem.