Zone and rule assisted recognition of Meitei-Mayek handwritten characters
Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hijam, Deena [verfasserIn] Saharia, Sarat [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Evolutionary intelligence - Springer Berlin Heidelberg, 2008, 17(2024), 4 vom: 21. März, Seite 2963-2980 |
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| Übergeordnetes Werk: |
volume:17 ; year:2024 ; number:4 ; day:21 ; month:03 ; pages:2963-2980 |
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| DOI / URN: |
10.1007/s12065-024-00920-z |
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SPR056552157 |
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| 520 | |a Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%. | ||
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| 650 | 4 | |a Two-stage |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Meitei Mayek |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Rule-based |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Optical character recognition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multistage |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Orthographic rules |7 (dpeaa)DE-He213 | |
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10.1007/s12065-024-00920-z doi (DE-627)SPR056552157 (SPR)s12065-024-00920-z-e DE-627 ger DE-627 rakwb eng 004 VZ Hijam, Deena verfasserin aut Zone and rule assisted recognition of Meitei-Mayek handwritten characters 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%. Handwritten character recognition (dpeaa)DE-He213 Two-stage (dpeaa)DE-He213 Confusing character pairs (dpeaa)DE-He213 Meitei Mayek (dpeaa)DE-He213 Rule-based (dpeaa)DE-He213 Optical character recognition (dpeaa)DE-He213 Multistage (dpeaa)DE-He213 Orthographic rules (dpeaa)DE-He213 Saharia, Sarat verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2024), 4 vom: 21. März, Seite 2963-2980 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2024 number:4 day:21 month:03 pages:2963-2980 https://dx.doi.org/10.1007/s12065-024-00920-z X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 4 21 03 2963-2980 |
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10.1007/s12065-024-00920-z doi (DE-627)SPR056552157 (SPR)s12065-024-00920-z-e DE-627 ger DE-627 rakwb eng 004 VZ Hijam, Deena verfasserin aut Zone and rule assisted recognition of Meitei-Mayek handwritten characters 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%. Handwritten character recognition (dpeaa)DE-He213 Two-stage (dpeaa)DE-He213 Confusing character pairs (dpeaa)DE-He213 Meitei Mayek (dpeaa)DE-He213 Rule-based (dpeaa)DE-He213 Optical character recognition (dpeaa)DE-He213 Multistage (dpeaa)DE-He213 Orthographic rules (dpeaa)DE-He213 Saharia, Sarat verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2024), 4 vom: 21. März, Seite 2963-2980 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2024 number:4 day:21 month:03 pages:2963-2980 https://dx.doi.org/10.1007/s12065-024-00920-z X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 4 21 03 2963-2980 |
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10.1007/s12065-024-00920-z doi (DE-627)SPR056552157 (SPR)s12065-024-00920-z-e DE-627 ger DE-627 rakwb eng 004 VZ Hijam, Deena verfasserin aut Zone and rule assisted recognition of Meitei-Mayek handwritten characters 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%. Handwritten character recognition (dpeaa)DE-He213 Two-stage (dpeaa)DE-He213 Confusing character pairs (dpeaa)DE-He213 Meitei Mayek (dpeaa)DE-He213 Rule-based (dpeaa)DE-He213 Optical character recognition (dpeaa)DE-He213 Multistage (dpeaa)DE-He213 Orthographic rules (dpeaa)DE-He213 Saharia, Sarat verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2024), 4 vom: 21. März, Seite 2963-2980 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2024 number:4 day:21 month:03 pages:2963-2980 https://dx.doi.org/10.1007/s12065-024-00920-z X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 4 21 03 2963-2980 |
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10.1007/s12065-024-00920-z doi (DE-627)SPR056552157 (SPR)s12065-024-00920-z-e DE-627 ger DE-627 rakwb eng 004 VZ Hijam, Deena verfasserin aut Zone and rule assisted recognition of Meitei-Mayek handwritten characters 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%. Handwritten character recognition (dpeaa)DE-He213 Two-stage (dpeaa)DE-He213 Confusing character pairs (dpeaa)DE-He213 Meitei Mayek (dpeaa)DE-He213 Rule-based (dpeaa)DE-He213 Optical character recognition (dpeaa)DE-He213 Multistage (dpeaa)DE-He213 Orthographic rules (dpeaa)DE-He213 Saharia, Sarat verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2024), 4 vom: 21. März, Seite 2963-2980 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2024 number:4 day:21 month:03 pages:2963-2980 https://dx.doi.org/10.1007/s12065-024-00920-z X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 4 21 03 2963-2980 |
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10.1007/s12065-024-00920-z doi (DE-627)SPR056552157 (SPR)s12065-024-00920-z-e DE-627 ger DE-627 rakwb eng 004 VZ Hijam, Deena verfasserin aut Zone and rule assisted recognition of Meitei-Mayek handwritten characters 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%. Handwritten character recognition (dpeaa)DE-He213 Two-stage (dpeaa)DE-He213 Confusing character pairs (dpeaa)DE-He213 Meitei Mayek (dpeaa)DE-He213 Rule-based (dpeaa)DE-He213 Optical character recognition (dpeaa)DE-He213 Multistage (dpeaa)DE-He213 Orthographic rules (dpeaa)DE-He213 Saharia, Sarat verfasserin aut Enthalten in Evolutionary intelligence Springer Berlin Heidelberg, 2008 17(2024), 4 vom: 21. März, Seite 2963-2980 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:17 year:2024 number:4 day:21 month:03 pages:2963-2980 https://dx.doi.org/10.1007/s12065-024-00920-z X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 4 21 03 2963-2980 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Handwritten character recognition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Two-stage</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Confusing character pairs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Meitei Mayek</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rule-based</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optical character recognition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multistage</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Orthographic rules</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Saharia, Sarat</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Evolutionary intelligence</subfield><subfield code="d">Springer Berlin Heidelberg, 2008</subfield><subfield code="g">17(2024), 4 vom: 21. 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Zone and rule assisted recognition of Meitei-Mayek handwritten characters |
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Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract This paper presents a two-stage approach for an improved recognition accuracy of handwritten Meitei Mayek characters. The first-stage is a CNN based recognition. The second-stage recognition is based on the zonal information and script-specific orthographic rules. The work is carried out on the finding that low recognition accuracy of Meitei Mayek handwritten characters is due to the presence of highly shape-similar confusing character pairs. First, the confusing character pairs are identified from the confusion matrix obtained while training and testing the CNN. For these confusing characters pairs, the zone information and certain script-specific orthographic rules are incorporated in the second recognition stage to distinguish between them. The second-stage recognition is carried out only on those characters which belong to one of the confusing character pairs. This makes the technique computationally more efficient than those where it is carried out for every output of the recognition stage. For zone identification, a novel method based on the row co-ordinates of characters present in the word is proposed. An improvement in the recognition accuracy is achieved with the two-stage approach compared to the single-stage recognition using the CNN. An improve ment in recognition accuracy of 3.36% is shown by our approach with a recognition accuracy of 91.86%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Zone and rule assisted recognition of Meitei-Mayek handwritten characters |
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https://dx.doi.org/10.1007/s12065-024-00920-z |
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|
| score |
7.401017 |