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.

In online character recognition based on elastic matching, such as dynamic programming matching, many of misrecognitions are often caused by overfitting, which is the phenomenon that the distance between reference pattern of an incorrect category and an input pattern is underestimated by unnatural matching. In this paper, a new recognition technique is proposed where category-specific deformations, called eigen-deformations, are utilized to suppress those misrecognitions. Generally, matching results at overfitting are not consistent with the eigen-deformations. Thus, the overfitting can be detected and penalized by a posterior evaluation of this inconsistency. The result of a recognition experiment showed the usefulness of the proposed technique. 1

A new hidden Markov model (HMM) is proposed for on-line character recognition using two typical features, pen-direction feature and pen-coordinate feature. These two features are quite different in their stationarity; pen-direction feature is stationary within every line segment of a stroke whereas pen-coordinate feature is not. In the proposed HMM, these contrasting features are used in a separative and selective way. Specifically speaking, pen-direction feature is outputted repeatedly at intra-state transition whereas pen-coordinate feature is outputted once at inter-state transition. The superiority of the proposed HMM over the conventional HMMs was shown through single-stroke and multi-stroke character recognition experiments. 1.

This paper proposes a new radical-based approach for online handwritten Chinese character recognition. The approach is novel in three respects: statistical classification of radicals, over-segmentation of characters into candidate radicals, and lexicon-driven recognition of characters. Currently, we have applied the approach to Chinese characters of left-right structure and are extending to other structures. Preliminary results on a sample set of 4,284 characters consisting of 1,118 radicals demonstrate the superiority of the proposed approach. 1.

We try to link elastic matching with a statistical discrimination framework to overcome the overfitting problem which often degrades the performance of elastic matchingbased online character recognizers. In the proposed technique, elastic matching is used just as an extractor of a feature vector representing the difference between input and reference patterns. Then quadratic discrimination is performed under the assumption that the feature vector is governed by a Gaussian distribution. The result of a recognition experiment on UNIPEN database (Train-R01/V07, 1a) showed that the proposed technique can attain a high recognition rate (97.95%) and outperforms a recent elastic matching-based recognizer. 1.

An on-line handwritten character recognition technique based on a new HMM is proposed. In the proposed HMM, not only pen-direction feature but also pen-coordinate feature are separately utilized for describing the shape variation of on-line characters accurately. Specifically speaking, the proposed HMM outputs a pen-coordinate feature at each inter-state transition and outputs a pen-direction feature at each intra-state transition, i.e., self-transition. Thus, each state of the proposed HMM can specify the starting position and the direction of a line segment by its incoming inter-state transition and intra-state transition, respectively. The results of recognition experiments on 10-stroke Chinese characters show that the proposed HMM outperforms the conventional HMM which does not use the pen-coordinate feature because of its non-stationarity. 1.

A new paradigm, which models the relationships between handwriting and topic categories, in the context of medical forms, is presented. The ultimate goals are (i) the recognition of medical handwriting, and (ii) the use of such information for practical applications such as a medical form search engine. Medical forms have diverse, complex and large lexicons consisting of English, Medical and Pharmacology corpus. Our technique shows that a few recognized characters, returned by handwriting recognition, can be used to construct a linguistic model capable of representing a medical topic

I would first of all like to give special thanks to Dr James Michael Hogan and Dr Jin hai Cai for supervising my honours project. Thank you to Jim for introducing me to localisation of software research area, and for all academic and personal help. I would like to thank Jim for providing revision for this thesis. I would like to thank Jin hai for providing technical advice and guiding me through the technical issues of HMMs. I would also like to thank Red Hat for supporting me financially this past year through the QUT Red Hat Honours Scholarship in Internationalization of Software. More importantly, I would like to than the team at Red Hat for great support, and for including me as part of their team. Especially, I would like to thank Leon Ho, Caius Chance and Jens Petersen. Your help, encouragement and support have meant a lot to me. 1 Redhat Linux is one of the leading Linux distributions in the market. Redhat Linux provides user friendly Chinese character input by using a keyboard short cut key combination mechanism, such as the popular “Pin Ying ” keyboard input method. As part

This paper describes a method of generating a Kanji hierarchical structured dictionary for stroke-number and stroke-order free handwriting recognition based on substroke HMM. In stroke-based methods, a large number of stroke-order variations can be easily expressed by just adding di#erent stroke sequences to the dictionary and it is not necessary to train new reference patterns. The hierarchical structured dictionary has an advantage that thousands of stroke-order variations of Kanji characters can be produced using a small number of stroke-order rules defining Kanji parts. Moreover, the recognition speed is fast since common sequences are shared in a substroke network, even if the total number of stroke-order combinations becomes enormous practically. In experiments, 300 di#erent stroke-order rules of Kanji parts were statistically chosen by using 60 writers' handwritings of 1,016 educational Kanji characters. By adding these new stroke-order rules to the dictionary, about 9,000 variations of di#erent stroke-orders were generated for 2,965 JIS 1st level Kanji characters. As a result, we successfully improved the recognition accuracy from 82.6% to 90.2% for stroke-order free handwritings.

This paper proposes a novel handwriting recognition interface for wearable computing where users write characters continuously without pauses on a small single writing box. Since characters are written on the same writing area, they are overlaid with each other. Therefore the task is regarded as a special case of the continuous character recognition problem. In contrast to the conventional continuous character recognition problem, location information of strokes does not help very much in the proposed framework. To tackle the problem, substroke based hidden Markov models (HMMs) and a stochastic bigram language model are employed. Preliminary experiments were carried out on a dataset of 578 handwriting sequences with a character bigram consisting of 1,016 Japanese educational Kanji and 71 Hiragana characters. The proposed method demonstrated promising performance with 69.2% of handwriting sequences beeing correctly recognized when different stroke order was permitted, and the rate was improved up to 88.0% when characters were written with fixed stroke order.