Handwritten devanagari manuscript characters recognition using capsnet
Manuscripts serve as a wealth of knowledge for future generations and are a useful source of information for locating material from the Middle Ages. Ancient manuscripts can be found in handwritten form, thus they must be translated into digital form so that computing equipment can access them and ad...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-06-01
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| Series: | International Journal of Cognitive Computing in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307423000049 |
| _version_ | 1827400256210337792 |
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| author | Aditi Moudgil Saravjeet Singh Vinay Gautam Shalli Rani Syed Hassan Shah |
| author_facet | Aditi Moudgil Saravjeet Singh Vinay Gautam Shalli Rani Syed Hassan Shah |
| author_sort | Aditi Moudgil |
| collection | DOAJ |
| description | Manuscripts serve as a wealth of knowledge for future generations and are a useful source of information for locating material from the Middle Ages. Ancient manuscripts can be found in handwritten form, thus they must be translated into digital form so that computing equipment can access them and additional indexing and search operations can be performed with ease. Manuscript recognition is already possible using a variety of methods. Regional languages like Devanagari, Gurmukhi, Sanskrit, etc., however, have very few methods available. In this study, the Devanagari characters from the manuscripts is recognised using a CapsNet-based method. 33 fundamental characters, 3 conjuncts, and 12 modifiers make up the Devanagari alphabet. The complete dataset is divided into 399 classes for the recognition of basic, modifiers, and conjunct characters. Due to spatial relationship, CapsNet is used to recognize the handwritten characters. The proposed model was run using 10:70, 20:80, and 30:70 as test: train ratio of characters. Also, the number of epochs was varied for better recognition accuracy. The authors observed the best recognition accuracy of 94.6% was achieved to recognize the Devanagari characters using CapsNet. |
| first_indexed | 2024-03-08T19:58:26Z |
| format | Article |
| id | doaj.art-ac6b87f129644175a0e58015072d31a7 |
| institution | Directory Open Access Journal |
| issn | 2666-3074 |
| language | English |
| last_indexed | 2024-03-08T19:58:26Z |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj.art-ac6b87f129644175a0e58015072d31a72023-12-24T04:46:35ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742023-06-0144754Handwritten devanagari manuscript characters recognition using capsnetAditi Moudgil0Saravjeet Singh1Vinay Gautam2Shalli Rani3Syed Hassan Shah4Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaCorresponding author.; Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaState California University, California, USAManuscripts serve as a wealth of knowledge for future generations and are a useful source of information for locating material from the Middle Ages. Ancient manuscripts can be found in handwritten form, thus they must be translated into digital form so that computing equipment can access them and additional indexing and search operations can be performed with ease. Manuscript recognition is already possible using a variety of methods. Regional languages like Devanagari, Gurmukhi, Sanskrit, etc., however, have very few methods available. In this study, the Devanagari characters from the manuscripts is recognised using a CapsNet-based method. 33 fundamental characters, 3 conjuncts, and 12 modifiers make up the Devanagari alphabet. The complete dataset is divided into 399 classes for the recognition of basic, modifiers, and conjunct characters. Due to spatial relationship, CapsNet is used to recognize the handwritten characters. The proposed model was run using 10:70, 20:80, and 30:70 as test: train ratio of characters. Also, the number of epochs was varied for better recognition accuracy. The authors observed the best recognition accuracy of 94.6% was achieved to recognize the Devanagari characters using CapsNet.http://www.sciencedirect.com/science/article/pii/S2666307423000049Optical character recognitionAncient documentsInnovative toolMachine learningHindi |
| spellingShingle | Aditi Moudgil Saravjeet Singh Vinay Gautam Shalli Rani Syed Hassan Shah Handwritten devanagari manuscript characters recognition using capsnet International Journal of Cognitive Computing in Engineering Optical character recognition Ancient documents Innovative tool Machine learning Hindi |
| title | Handwritten devanagari manuscript characters recognition using capsnet |
| title_full | Handwritten devanagari manuscript characters recognition using capsnet |
| title_fullStr | Handwritten devanagari manuscript characters recognition using capsnet |
| title_full_unstemmed | Handwritten devanagari manuscript characters recognition using capsnet |
| title_short | Handwritten devanagari manuscript characters recognition using capsnet |
| title_sort | handwritten devanagari manuscript characters recognition using capsnet |
| topic | Optical character recognition Ancient documents Innovative tool Machine learning Hindi |
| url | http://www.sciencedirect.com/science/article/pii/S2666307423000049 |
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