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The efficiency of the NSHPZ-HMM: theoretical and practical study

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Abstract

In this paper, we propose a novel HMM-based 2-D recognition engine, namely the NSHPZ-HMM. Like the reference model (the NSHP-HMM), the proposed classifier brings the efficient training and decoding algorithms of 1-D HMM to the 2-D modeling of spatial data. Furthermore, in contrast to the reference model which suffers from the short 2-D context limitation, our model uses the NSHP Markov random field to describe the contextual information at a ’zone’ level rather than a ’pixel’ level; the goal is to extend the context in order to give a better modeling of the spatial property of an image. Therefore, the use of high-level features extracted directly on the gray-level or color zones is possible, unlike what is done in a recognition based on classical NSHP-HMM, where the model, mandatorily, operates at a pixel level on normalized binary images; consequently, the applicability of our model is more general compared to the classical NSHP-HMM. Throughout this paper, we demonstrate the efficiency of the proposed approach at two stages. Firstly, in the theoretical study, we show the advantage of our model over other HMM-based 2-D classifiers; this part constitutes by itself, to our knowledge, the first complete overview of 2-D recognition approaches. Secondly, the experimental evaluation performed on recognition of handwritten digits/words provides the effectiveness of the NSHPZ-HMM against all other HMM-based 2-D recognizers and shows a good potential for other image recognition applications.

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Notes

  1. We note that the ’site’ can corresponds to one pixel or a bloc of pixels.

  2. Other studies such as 2[38] consider the order as the distance between sites. Thus, according to the latter view, the HMMRF defined here is a first-order Markovian model.

  3. The corresponding solution for each drawback is noted by (’ ), i.e. (a’) is the proposed solution of drawback (a).

  4. The site (i, j) corresponds to the pixel (i, j) for the NSHP-HMM model and to the zone \(Z_{ij}\) for the NSHPZ-HMM.

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Author information

Authors and Affiliations

  1. ENSET(Skikda), LabGed, Annaba University, Annaba, Algeria

    Hanene Boukerma

  2. ETS, Synchromedia Lab, Montreal, QC, Canada

    Hanene Boukerma & Mohamed Cheriet

  3. ALTRAN, Montpellier, France

    Christophe Choisy

  4. LabGedAnnaba University, Annaba, Algeria

    Nadir Farah

Authors
  1. Hanene Boukerma
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  2. Christophe Choisy
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  3. Nadir Farah
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  4. Mohamed Cheriet
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Correspondence to Hanene Boukerma.

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Boukerma, H., Choisy, C., Farah, N. et al. The efficiency of the NSHPZ-HMM: theoretical and practical study. Appl Intell 48, 4660–4677 (2018). https://doi.org/10.1007/s10489-018-1217-z

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