A successful handwritten word recognition (HWR) system using a variable duration hidden Markov model (VDHMM) and the path discriminant-HMM (PD-HMM) strategy is easy to implement. The central theme of the paper is to show that if the duration statistics are computed, it could be utilized to implement a model-discriminant-HMM (MD-HMM) approach for better experimental results. The paper also describes a PD-HMM based HWR system where the duration statistics are not explicitly computed, but results are still comparable to VDHMM based HWR scheme.

We describe an MD-HMM (model discriminant HMM) based handwritten word recognition (HWR) system (called NEHMM-nonergodic HMM) whose system parameters are derived from the parameters of the VDHMM (variable duration HMM) system described by Chen, Kundu and Srihari (see IEEE Trans. on Image Proc., vol.4, no.12, p.1675-88, 1995). The new HMM achieves better experimental results by a more efficient utilization of variable duration information. However, more often the problem is 'reliable computation' of duration probabilities given limited databases. A scheme (VSLHMM-variable sequence length HMM) has been presented to avoid the computation of duration probabilities altogether without sacrificing the performance gain of the VDHMM system.

We introduce a novel approach for handwritten word recognition using multilevel hidden Markov models (MLHMM). The MLHMM is a doubly embedded network of HMMs where each character is modeled by an HMM while a word is modeled by a higher-level HMM. In the character model, we associate the observation with the transition. By introducing the technique called 'tied transition', i.e., the segments which have the same semantic meaning will be 'tied' together, we have successfully built up the character model by an HMM with 4 states, 5 observations (or symbols) and 7 transitions. Thus, as the states are not assigned any semantic meaning, the re-estimation algorithm is applicable. At the character level, the best model is chosen as the recognition result. So, the character model is purely a model discriminant HMM (MD-HMM) based approach. For the word model, on the other hand, both the MD-HMM and the path discriminant HMM (PD-HMM) approaches are used and their respective performances are demonstrated.

Because of large variation involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used both in speech and handwriting recognition. Basically, there are two strategies of using HMM: model discriminant HMM (MD-HMM) and path discriminant HMM (PD-HMM). Both of them have their advantages and disadvantages, and are discussed in this paper. Chen, Kundu and Sihari (see Proc. IEEE Int. Conference on Acoust., Speech, Signal Processing, (Minneapolis, Minnesota), p.V.105-108, April 1993) have developed a handwritten word recognition system using continuous density variable duration hidden Markov model (CDVDHMM), which belongs to the PD-HMM strategy. We describe a MD-HMM approach with the statistics derived from the CDVDHMM parameters. Detailed experiments are carried out; and the results using different approaches are compared.>

In this paper, we have developed a handwritten word recognition scheme based on a single contextual hidden Markov model (HMM) incorporated with an adaptive length Viterbi algorithm. This work attempts to extend our earlier HMM scheme for naturally segmented word recognition to cursive and nonsegmented word recognition. The algorithm pre-segments the script into characters and/or fractions of characters, dynamically selects the optimal segmentation points, determines the word length, and recognizes the word according to the maximum path probability. The HMM is on top of, but independent of, script segmentation and character recognition techniques, and therefore can be further improved by incorporating more refined segmentation and character recognition procedure. The experiments have shown promising results.

Because of large variations involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used in speech processing and recognition. Recently HMM has also been used with some success in recognizing handwritten words with presegmented letters. In this paper, a complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model type stochastic network is presented. Our scheme includes a morphology and heuristics based segmentation algorithm, a training algorithm that can adapt itself with the changing dictionary, and a modified Viterbi algorithm which searches for the (l+1)th globally best path based on the previous l best paths. Detailed experiments are carried out and successful recognition results are reported.>

A complete system for the recognition of unconstrained handwritten words using a continuous density variable duration hidden Markov model (CDVDHMM) is described. A new segmentation algorithm based on mathematical morphology is used to translate the 2-D image into a 1-D sequence of sub-character symbols. This sequence of symbols is modeled by the CDVDHMM. Generally, there are two information sources associated with the written text. While the shape information of each character symbol is modeled as a mixture Gaussian distribution, the linguistic knowledge, i.e., constraint, is modeled as a Markov chain. In this context, the variable duration state is used to take care of the segmentation ambiguity among the consecutive characters. Some experimental results are described to demonstrate the success of the proposed scheme.>

A complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model (HMM) is proposed. The scheme includes a morphology- and heuristics-based segmentation algorithm and a modified Viterbi algorithm that searches the (l+1)st globally best path based on the previous l best paths. The results of detailed experiments for which the overall recognition rate is up to 89.4% are reported.>

The authors have developed a handwritten word recognition scheme based on a single contextual, discrete symbol probability hidden Markov model (HMM) incorporated with an adaptive length Viterbi algorithm. This work attempts to extend the earlier HMM scheme for naturally segmented word recognition to cursive and nonsegmented word recognition. The algorithm presegments the script into characters and/or fractions of characters, dynamically selects the correct segmentation points, determines the word length, and recognizes the word according to the maximum path probability. The HMM is on top of, but independent of, script segmentation and character recognition techniques, and therefore leaves room for further improvement. The experiments have shown promising results and directions for further improvement.>