Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology
Abstract To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative st...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Si, Huihui [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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. |
|---|
| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 28(2023), 1 vom: 11. Dez., Seite 721-736 |
|---|---|
| Übergeordnetes Werk: |
volume:28 ; year:2023 ; number:1 ; day:11 ; month:12 ; pages:721-736 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-023-09423-y |
|---|
| Katalog-ID: |
SPR054258618 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR054258618 | ||
| 003 | DE-627 | ||
| 005 | 20240105064714.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240105s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00500-023-09423-y |2 doi | |
| 035 | |a (DE-627)SPR054258618 | ||
| 035 | |a (SPR)s00500-023-09423-y-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Si, Huihui |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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. | ||
| 520 | |a Abstract To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. | ||
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Computer-aided technology |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Calligraphy Chinese characters |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Recognition technology |7 (dpeaa)DE-He213 | |
| 773 | 0 | 8 | |i Enthalten in |t Soft Computing |d Springer-Verlag, 2003 |g 28(2023), 1 vom: 11. Dez., Seite 721-736 |w (DE-627)SPR006469531 |7 nnns |
| 773 | 1 | 8 | |g volume:28 |g year:2023 |g number:1 |g day:11 |g month:12 |g pages:721-736 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00500-023-09423-y |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 28 |j 2023 |e 1 |b 11 |c 12 |h 721-736 | ||
| author_variant |
h s hs |
|---|---|
| matchkey_str |
sihuihui:2023----:nlssfalgahcieehrcercgiinehooyaeodelan |
| hierarchy_sort_str |
2023 |
| publishDate |
2023 |
| allfields |
10.1007/s00500-023-09423-y doi (DE-627)SPR054258618 (SPR)s00500-023-09423-y-e DE-627 ger DE-627 rakwb eng Si, Huihui verfasserin aut Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology 2023 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 2023. 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 To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. Deep learning (dpeaa)DE-He213 Computer-aided technology (dpeaa)DE-He213 Calligraphy Chinese characters (dpeaa)DE-He213 Recognition technology (dpeaa)DE-He213 Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 11. Dez., Seite 721-736 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:11 month:12 pages:721-736 https://dx.doi.org/10.1007/s00500-023-09423-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 11 12 721-736 |
| spelling |
10.1007/s00500-023-09423-y doi (DE-627)SPR054258618 (SPR)s00500-023-09423-y-e DE-627 ger DE-627 rakwb eng Si, Huihui verfasserin aut Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology 2023 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 2023. 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 To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. Deep learning (dpeaa)DE-He213 Computer-aided technology (dpeaa)DE-He213 Calligraphy Chinese characters (dpeaa)DE-He213 Recognition technology (dpeaa)DE-He213 Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 11. Dez., Seite 721-736 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:11 month:12 pages:721-736 https://dx.doi.org/10.1007/s00500-023-09423-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 11 12 721-736 |
| allfields_unstemmed |
10.1007/s00500-023-09423-y doi (DE-627)SPR054258618 (SPR)s00500-023-09423-y-e DE-627 ger DE-627 rakwb eng Si, Huihui verfasserin aut Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology 2023 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 2023. 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 To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. Deep learning (dpeaa)DE-He213 Computer-aided technology (dpeaa)DE-He213 Calligraphy Chinese characters (dpeaa)DE-He213 Recognition technology (dpeaa)DE-He213 Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 11. Dez., Seite 721-736 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:11 month:12 pages:721-736 https://dx.doi.org/10.1007/s00500-023-09423-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 11 12 721-736 |
| allfieldsGer |
10.1007/s00500-023-09423-y doi (DE-627)SPR054258618 (SPR)s00500-023-09423-y-e DE-627 ger DE-627 rakwb eng Si, Huihui verfasserin aut Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology 2023 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 2023. 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 To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. Deep learning (dpeaa)DE-He213 Computer-aided technology (dpeaa)DE-He213 Calligraphy Chinese characters (dpeaa)DE-He213 Recognition technology (dpeaa)DE-He213 Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 11. Dez., Seite 721-736 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:11 month:12 pages:721-736 https://dx.doi.org/10.1007/s00500-023-09423-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 11 12 721-736 |
| allfieldsSound |
10.1007/s00500-023-09423-y doi (DE-627)SPR054258618 (SPR)s00500-023-09423-y-e DE-627 ger DE-627 rakwb eng Si, Huihui verfasserin aut Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology 2023 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 2023. 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 To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. Deep learning (dpeaa)DE-He213 Computer-aided technology (dpeaa)DE-He213 Calligraphy Chinese characters (dpeaa)DE-He213 Recognition technology (dpeaa)DE-He213 Enthalten in Soft Computing Springer-Verlag, 2003 28(2023), 1 vom: 11. Dez., Seite 721-736 (DE-627)SPR006469531 nnns volume:28 year:2023 number:1 day:11 month:12 pages:721-736 https://dx.doi.org/10.1007/s00500-023-09423-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 28 2023 1 11 12 721-736 |
| language |
English |
| source |
Enthalten in Soft Computing 28(2023), 1 vom: 11. Dez., Seite 721-736 volume:28 year:2023 number:1 day:11 month:12 pages:721-736 |
| sourceStr |
Enthalten in Soft Computing 28(2023), 1 vom: 11. Dez., Seite 721-736 volume:28 year:2023 number:1 day:11 month:12 pages:721-736 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Deep learning Computer-aided technology Calligraphy Chinese characters Recognition technology |
| isfreeaccess_bool |
false |
| container_title |
Soft Computing |
| authorswithroles_txt_mv |
Si, Huihui @@aut@@ |
| publishDateDaySort_date |
2023-12-11T00:00:00Z |
| hierarchy_top_id |
SPR006469531 |
| id |
SPR054258618 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR054258618</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240105064714.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240105s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-023-09423-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR054258618</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-023-09423-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Si, Huihui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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 To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer-aided technology</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Calligraphy Chinese characters</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Recognition technology</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">28(2023), 1 vom: 11. Dez., Seite 721-736</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:28</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:11</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:721-736</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-023-09423-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">28</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">11</subfield><subfield code="c">12</subfield><subfield code="h">721-736</subfield></datafield></record></collection>
|
| author |
Si, Huihui |
| spellingShingle |
Si, Huihui misc Deep learning misc Computer-aided technology misc Calligraphy Chinese characters misc Recognition technology Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology |
| authorStr |
Si, Huihui |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)SPR006469531 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology Deep learning (dpeaa)DE-He213 Computer-aided technology (dpeaa)DE-He213 Calligraphy Chinese characters (dpeaa)DE-He213 Recognition technology (dpeaa)DE-He213 |
| topic |
misc Deep learning misc Computer-aided technology misc Calligraphy Chinese characters misc Recognition technology |
| topic_unstemmed |
misc Deep learning misc Computer-aided technology misc Calligraphy Chinese characters misc Recognition technology |
| topic_browse |
misc Deep learning misc Computer-aided technology misc Calligraphy Chinese characters misc Recognition technology |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Soft Computing |
| hierarchy_parent_id |
SPR006469531 |
| hierarchy_top_title |
Soft Computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)SPR006469531 |
| title |
Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology |
| ctrlnum |
(DE-627)SPR054258618 (SPR)s00500-023-09423-y-e |
| title_full |
Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology |
| author_sort |
Si, Huihui |
| journal |
Soft Computing |
| journalStr |
Soft Computing |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| container_start_page |
721 |
| author_browse |
Si, Huihui |
| container_volume |
28 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Si, Huihui |
| doi_str_mv |
10.1007/s00500-023-09423-y |
| title_sort |
analysis of calligraphy chinese character recognition technology based on deep learning and computer-aided technology |
| title_auth |
Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology |
| abstract |
Abstract To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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 To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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 To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER |
| container_issue |
1 |
| title_short |
Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology |
| url |
https://dx.doi.org/10.1007/s00500-023-09423-y |
| remote_bool |
true |
| ppnlink |
SPR006469531 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00500-023-09423-y |
| up_date |
2024-07-04T00:43:55.500Z |
| _version_ |
1803607170614296576 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR054258618</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240105064714.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240105s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-023-09423-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR054258618</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-023-09423-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Si, Huihui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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 To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network’s (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer-aided technology</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Calligraphy Chinese characters</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Recognition technology</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">28(2023), 1 vom: 11. Dez., Seite 721-736</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:28</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:11</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:721-736</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-023-09423-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">28</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">11</subfield><subfield code="c">12</subfield><subfield code="h">721-736</subfield></datafield></record></collection>
|
| score |
7.3969316 |