Handwriting perceptual classification and synthesis using discriminate HMMs and progressive iterative approximation
Abstract This paper handles the problem of online handwriting synthesis. Indeed, this work presents a probabilistic model using the embedded hidden Markov models (HMMs) for the classification and modeling of perceptual sequences. At first, we start with a vector of perceptual points as input seeking...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bezine, Hala [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 32(2019), 21 vom: 25. Apr., Seite 16549-16570 |
|---|---|
| Übergeordnetes Werk: |
volume:32 ; year:2019 ; number:21 ; day:25 ; month:04 ; pages:16549-16570 |
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| DOI / URN: |
10.1007/s00521-019-04206-9 |
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| 520 | |a Abstract This paper handles the problem of online handwriting synthesis. Indeed, this work presents a probabilistic model using the embedded hidden Markov models (HMMs) for the classification and modeling of perceptual sequences. At first, we start with a vector of perceptual points as input seeking a class of basic shape probability as output. In fact, these perceptual points are necessary for the drawing and the recovering of each basic shape where each one is designed with an HMM built and trained with its components. Each path through these possibilities of control points represents an observation that serves as input for the following step. Secondly, the already detected sequences of observations which represent a segment formed an initial HMM and the concatenation of multiple ones leads to a global HMM. To classify a global HMM, we should codify it by searching the best path of initial HMM. The best path is obtained by computing the maximum of likelihood of the different basic shapes. In order to synthesize the handwritten trace, and to recover the best control points sequences, we investigated the progressive iterative approximation. The performance of the proposed model was assessed using samples of scripts extracted from IRONOFF and MAYASTROON databases. In fact, these samples served for the generation of the set of control points used for the HMMs training models. In experiments, good quantitative agreement and approximation were found for the generated trajectories and a more reduced representation of the scripts models was designed. | ||
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10.1007/s00521-019-04206-9 doi (DE-627)OLC2120012504 (DE-He213)s00521-019-04206-9-p DE-627 ger DE-627 rakwb eng 004 VZ Bezine, Hala verfasserin aut Handwriting perceptual classification and synthesis using discriminate HMMs and progressive iterative approximation 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract This paper handles the problem of online handwriting synthesis. Indeed, this work presents a probabilistic model using the embedded hidden Markov models (HMMs) for the classification and modeling of perceptual sequences. At first, we start with a vector of perceptual points as input seeking a class of basic shape probability as output. In fact, these perceptual points are necessary for the drawing and the recovering of each basic shape where each one is designed with an HMM built and trained with its components. Each path through these possibilities of control points represents an observation that serves as input for the following step. Secondly, the already detected sequences of observations which represent a segment formed an initial HMM and the concatenation of multiple ones leads to a global HMM. To classify a global HMM, we should codify it by searching the best path of initial HMM. The best path is obtained by computing the maximum of likelihood of the different basic shapes. In order to synthesize the handwritten trace, and to recover the best control points sequences, we investigated the progressive iterative approximation. The performance of the proposed model was assessed using samples of scripts extracted from IRONOFF and MAYASTROON databases. In fact, these samples served for the generation of the set of control points used for the HMMs training models. In experiments, good quantitative agreement and approximation were found for the generated trajectories and a more reduced representation of the scripts models was designed. Cursive handwriting synthesis Embedded hidden Markov models Visual perceptual codes Control points Progressive iterative interpolation Alimi, Adel M. aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 21 vom: 25. Apr., Seite 16549-16570 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:21 day:25 month:04 pages:16549-16570 https://doi.org/10.1007/s00521-019-04206-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 21 25 04 16549-16570 |
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10.1007/s00521-019-04206-9 doi (DE-627)OLC2120012504 (DE-He213)s00521-019-04206-9-p DE-627 ger DE-627 rakwb eng 004 VZ Bezine, Hala verfasserin aut Handwriting perceptual classification and synthesis using discriminate HMMs and progressive iterative approximation 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract This paper handles the problem of online handwriting synthesis. Indeed, this work presents a probabilistic model using the embedded hidden Markov models (HMMs) for the classification and modeling of perceptual sequences. At first, we start with a vector of perceptual points as input seeking a class of basic shape probability as output. In fact, these perceptual points are necessary for the drawing and the recovering of each basic shape where each one is designed with an HMM built and trained with its components. Each path through these possibilities of control points represents an observation that serves as input for the following step. Secondly, the already detected sequences of observations which represent a segment formed an initial HMM and the concatenation of multiple ones leads to a global HMM. To classify a global HMM, we should codify it by searching the best path of initial HMM. The best path is obtained by computing the maximum of likelihood of the different basic shapes. In order to synthesize the handwritten trace, and to recover the best control points sequences, we investigated the progressive iterative approximation. The performance of the proposed model was assessed using samples of scripts extracted from IRONOFF and MAYASTROON databases. In fact, these samples served for the generation of the set of control points used for the HMMs training models. In experiments, good quantitative agreement and approximation were found for the generated trajectories and a more reduced representation of the scripts models was designed. Cursive handwriting synthesis Embedded hidden Markov models Visual perceptual codes Control points Progressive iterative interpolation Alimi, Adel M. aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 21 vom: 25. Apr., Seite 16549-16570 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:21 day:25 month:04 pages:16549-16570 https://doi.org/10.1007/s00521-019-04206-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 21 25 04 16549-16570 |
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10.1007/s00521-019-04206-9 doi (DE-627)OLC2120012504 (DE-He213)s00521-019-04206-9-p DE-627 ger DE-627 rakwb eng 004 VZ Bezine, Hala verfasserin aut Handwriting perceptual classification and synthesis using discriminate HMMs and progressive iterative approximation 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract This paper handles the problem of online handwriting synthesis. Indeed, this work presents a probabilistic model using the embedded hidden Markov models (HMMs) for the classification and modeling of perceptual sequences. At first, we start with a vector of perceptual points as input seeking a class of basic shape probability as output. In fact, these perceptual points are necessary for the drawing and the recovering of each basic shape where each one is designed with an HMM built and trained with its components. Each path through these possibilities of control points represents an observation that serves as input for the following step. Secondly, the already detected sequences of observations which represent a segment formed an initial HMM and the concatenation of multiple ones leads to a global HMM. To classify a global HMM, we should codify it by searching the best path of initial HMM. The best path is obtained by computing the maximum of likelihood of the different basic shapes. In order to synthesize the handwritten trace, and to recover the best control points sequences, we investigated the progressive iterative approximation. The performance of the proposed model was assessed using samples of scripts extracted from IRONOFF and MAYASTROON databases. In fact, these samples served for the generation of the set of control points used for the HMMs training models. In experiments, good quantitative agreement and approximation were found for the generated trajectories and a more reduced representation of the scripts models was designed. Cursive handwriting synthesis Embedded hidden Markov models Visual perceptual codes Control points Progressive iterative interpolation Alimi, Adel M. aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 21 vom: 25. Apr., Seite 16549-16570 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:21 day:25 month:04 pages:16549-16570 https://doi.org/10.1007/s00521-019-04206-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 21 25 04 16549-16570 |
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Handwriting perceptual classification and synthesis using discriminate HMMs and progressive iterative approximation |
| abstract |
Abstract This paper handles the problem of online handwriting synthesis. Indeed, this work presents a probabilistic model using the embedded hidden Markov models (HMMs) for the classification and modeling of perceptual sequences. At first, we start with a vector of perceptual points as input seeking a class of basic shape probability as output. In fact, these perceptual points are necessary for the drawing and the recovering of each basic shape where each one is designed with an HMM built and trained with its components. Each path through these possibilities of control points represents an observation that serves as input for the following step. Secondly, the already detected sequences of observations which represent a segment formed an initial HMM and the concatenation of multiple ones leads to a global HMM. To classify a global HMM, we should codify it by searching the best path of initial HMM. The best path is obtained by computing the maximum of likelihood of the different basic shapes. In order to synthesize the handwritten trace, and to recover the best control points sequences, we investigated the progressive iterative approximation. The performance of the proposed model was assessed using samples of scripts extracted from IRONOFF and MAYASTROON databases. In fact, these samples served for the generation of the set of control points used for the HMMs training models. In experiments, good quantitative agreement and approximation were found for the generated trajectories and a more reduced representation of the scripts models was designed. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstractGer |
Abstract This paper handles the problem of online handwriting synthesis. Indeed, this work presents a probabilistic model using the embedded hidden Markov models (HMMs) for the classification and modeling of perceptual sequences. At first, we start with a vector of perceptual points as input seeking a class of basic shape probability as output. In fact, these perceptual points are necessary for the drawing and the recovering of each basic shape where each one is designed with an HMM built and trained with its components. Each path through these possibilities of control points represents an observation that serves as input for the following step. Secondly, the already detected sequences of observations which represent a segment formed an initial HMM and the concatenation of multiple ones leads to a global HMM. To classify a global HMM, we should codify it by searching the best path of initial HMM. The best path is obtained by computing the maximum of likelihood of the different basic shapes. In order to synthesize the handwritten trace, and to recover the best control points sequences, we investigated the progressive iterative approximation. The performance of the proposed model was assessed using samples of scripts extracted from IRONOFF and MAYASTROON databases. In fact, these samples served for the generation of the set of control points used for the HMMs training models. In experiments, good quantitative agreement and approximation were found for the generated trajectories and a more reduced representation of the scripts models was designed. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract This paper handles the problem of online handwriting synthesis. Indeed, this work presents a probabilistic model using the embedded hidden Markov models (HMMs) for the classification and modeling of perceptual sequences. At first, we start with a vector of perceptual points as input seeking a class of basic shape probability as output. In fact, these perceptual points are necessary for the drawing and the recovering of each basic shape where each one is designed with an HMM built and trained with its components. Each path through these possibilities of control points represents an observation that serves as input for the following step. Secondly, the already detected sequences of observations which represent a segment formed an initial HMM and the concatenation of multiple ones leads to a global HMM. To classify a global HMM, we should codify it by searching the best path of initial HMM. The best path is obtained by computing the maximum of likelihood of the different basic shapes. In order to synthesize the handwritten trace, and to recover the best control points sequences, we investigated the progressive iterative approximation. The performance of the proposed model was assessed using samples of scripts extracted from IRONOFF and MAYASTROON databases. In fact, these samples served for the generation of the set of control points used for the HMMs training models. In experiments, good quantitative agreement and approximation were found for the generated trajectories and a more reduced representation of the scripts models was designed. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
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| title_short |
Handwriting perceptual classification and synthesis using discriminate HMMs and progressive iterative approximation |
| url |
https://doi.org/10.1007/s00521-019-04206-9 |
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| author2 |
Alimi, Adel M. |
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| doi_str |
10.1007/s00521-019-04206-9 |
| up_date |
2024-07-04T02:58:04.469Z |
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