This paper describes a scheme to adapt the parameters of a tied-mixture, hidden Markov model, on-line handwriting recognition system to improve performance on new writers' handwriting. The means and variances of the distributions are adapted using the maximum likelihood linear regression technique. Experiments are performed with a number of new writers in both supervised and unsupervised modes. Adaptation on data quantities as small as 5 words is found to result in models with 6% lower error rate than the writer independent model.

In a hidden Markov model system, the initialization of the model parameters is critical to the performance of the model after retraining. This paper proposes a number of new approaches to the problem of initialization, and demonstrates that a method of smooth alignment results in the best performance.

This paper describes a series of experiments that have been conducted to investigate the effect of duration modeling in a hidden Markov model (HMM) based online handwriting recognition system. The issues discussed include parametric vs. non-parametric distributions to model duration, different methods for training transition probabilities and the effect of weighting on duration terms.

In this paper, we present a framework for adapting a writer independent system to a user from samples of the user's writing. The writer independent system is modeled using hidden Markov models. Training for a writer involves recomputing the topology and parameters of the hidden Markov models using the writer's data. The framework uses the writer independent system to get an initial alignment of the writer's data. The system described reduces the error rate by an average of 65%. For the results presented, no language model was used.

We describe experiments on a writer dependent large vocabulary (20000) handwriting recognition system. The goal was to investigate the effects of different degrees of tying (and hence, of the number) of parameters on the error rate. The system recognizes cursive, printed or any combination thereof of script in real time on small PC platforms. Baseline results for a writer independent system are also included. Since we are only interested in the shape models no language models were used.

One area where on-line handwriting recognition technology is most critical is the domain of small portable platforms. Because such platforms have limited resources, it is not presently practical to consider a continuous parameterization for the hidden Markov models used in the recognition. On the other hand, discrete parameter techniques such as used in speech recognition are difficult to apply, because there is no well-understood handwriting equivalent to phonological rules. A possible solution is to extract this information directly from the data, by constructing an alphabet of sub-character, elementary handwriting units. The performance of this method is illustrated on a discrete handwriting recognition task with an alphabet of 81 characters.

We address the problem of automatic recognition of unconstrained handwritten text. Statistical methods, such as hidden Markov models (HMMs) have been used successfully for speech recognition and they have been applied to the problem of handwriting recognition as well. We discuss a general recognition system for large vocabulary, writer independent, unconstrained handwritten text. "Unconstrained" implies that the user may write in any style e.g. printed, cursive or in any combination of styles. This is more representative of typical handwritten text where one seldom encounters purely printed or purely cursive forms. Furthermore, a key characteristic of the system is that it performs recognition in real-time on 486 class PC platforms without the large amounts of memory required for traditional HMM based systems. We focus mainly on the writer independent task. Some initial writer dependent results are also reported. An error rate of 18.9% is achieved for a writer-independent 21,000 word vocabulary task in the absence of any language models.

The performance of a large alphabet handwriting recognition system based on a probabilistic framework is critically tied to the quality of the prototype distributions that are established in the relevant feature space. To better account for handwriting variability, we describe a supervised strategy for the construction of prototype distributions which are more robust to allograph deformations. The idea is to incorporate supervision to relate the allographic models to their manifestations in the feature space. This makes for a better utilization of the available training data, while at the same time allowing for a short design time turn around. The performance of this method is illustrated on a discrete handwriting recognition task with an alphabet of 81 characters.>

In an on-line handwriting recognition system, the motion of the tip of the stylus (pen) is sampled at equal time intervals using a digitizer tablet and the sampled points are passed to a computer which performs the handwriting recognition. In most cases, the basic recognition algorithm performs best for a nominal size of writing as well as a standard orientation (normally horizontal) and a nominal slant (normally fully upright). We discuss and provide solutions to these normalization problems in the context of on-line handwriting recognition. Most of the results presented are also valid for optical character recognition (OCR). Error rate reductions of 54.3% and 35.8% were obtained for the writer-dependent and writer-independent samples through using the proposed normalization scheme.>

The automatic recognition of online handwriting is considered from an information theoretic viewpoint. Emphasis is placed on the recognition of unconstrained handwriting, a general combination of cursively written word fragments and discretely written characters. Existing recognition algorithms, such as elastic matching, are severely challenged by the variability inherent in unconstrained handwriting. This motivates the development of a probabilistic framework suitable for the derivation of a fast statistical mixture algorithm. This algorithm exhibits about the same degree of complexity as elastic matching, while being more flexible and potentially more robust. The approach relies on a novel front-end processor that, unlike conventional character or stroke-based processing, articulates around a small elementary unit of handwriting called a frame. The algorithm is based on (1) producing feature vectors representing each frame in one (or several) feature spaces, (2) Gaussian K-means clustering in these spaces, and (3) mixture modeling, taking into account the contributions of all relevant clusters in each space. The approach is illustrated by a simple task involving an 81-character alphabet. Both writer-dependent and writer-independent recognition results are found to be competitive with their elastic matching counterparts.>

A feature set that captures the dynamics of formant transitions prior to closure in a VCV environment is used to characterize and classify the unvoiced stop consonants. The feature set is derived from a time-varying, data-selective model for the speech signal. Its performance is compared with that of comparable formant data from a standard delta-LPC-based model. The different feature sets are evaluated on a database composed of eight talkers. A 40% reduction in classification error rate is obtained by means of the time-varying model. The performance of three different classifiers is discussed. A novel adaptive algorithm, termed learning vector classifier (LVC) is compared with standard K-means and LVQ2 classifiers. LVC is a supervised learning classifier that improves performance by increasing the resolution of the decision boundaries. Error rates obtained for the three-way (p, t, and k) classification task using LVC and the time-varying analysis are comparable to that of techniques that make use of additional discriminating information contained in the burst. Further improvements are expected when an expanded time-varying feature set is utilized, coupled with information from the burst.>

A feature set that captures the dynamics of formant transitions is utilized to classify the unvoiced stop consonants. The second formant and its slope are used to characterize the transition between the vowel and the closure in a VCV (vowel-consonant-vowel) environment. The performance of a feature set obtained by means of a time-varying, closed-glottis model for the signal is compared with that of a standard LPC (linear predictive coding) model. The different feature sets are evaluated on a database consisting of eight speakers. A fourfold reduction in the error rate is obtained by means of the more sophisticated model. The performance of three different classifiers is presented. A novel adaptive algorithm, the learning vector classifier, is compared with standard K-means and LVQ2 (learning vector quantization-2) classifiers. Error rates of 5% are obtained for the three-way classification.>

Two data-selective techniques that facilitate formant tracking with greater temporal resolution are presented: a time-varying autoregressive (AR) model and a short window AR model. They are used to analyze the regions of voicing immediately preceding closure in vowel-consonant transitions. Results on real speech data show that these methods detect the rapid change in the formants prior to closure better than standard linear predictive coding methods; hence, they provide important cues as to the nature of the stop. When these cues are used in conjunction with the commonly used ones from the burst, far more accurate labeling of the stops should result.>