An Idea to Recognition of handwritten Characters using Genetic Algorithms Article Swipe
Samta Jaın Goyal
,
Rajeev Goyal
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.14776379
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.14776379
Challenges in handwritten characters recognition is due to the variation and distortion of handwritten characters, since different people use different style and way of draw the same shape of the characters. This paper demonstrates the nature of handwritten characters, conversion of handwritten data into electronic data, and the neural network approach to make machine capable of recognizing hand written characters. This motivates the use of Genetic Algorithms for the problem. In order to prove this, we made a pool of images of characters. We converted them to graphs. The graph of every character was intermixed to generate new or unique styles intermediate between the styles of parent character. Character recognition involved the matching of the graph generated from the unknown character image with the graphs generated by mixing.
Related Topics
Concepts
Character (mathematics)
Computer science
Character recognition
Intelligent word recognition
Artificial intelligence
Matching (statistics)
Artificial neural network
Intelligent character recognition
Pattern recognition (psychology)
Variation (astronomy)
Graph
Distortion (music)
Image (mathematics)
Mathematics
Theoretical computer science
Statistics
Amplifier
Computer network
Geometry
Physics
Bandwidth (computing)
Astrophysics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.14776379
- OA Status
- green
- References
- 15
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3035819829
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3035819829Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.14776379Digital Object Identifier
- Title
-
An Idea to Recognition of handwritten Characters using Genetic AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-04-30Full publication date if available
- Authors
-
Samta Jaın Goyal, Rajeev GoyalList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.14776379Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5281/zenodo.14776379Direct OA link when available
- Concepts
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Character (mathematics), Computer science, Character recognition, Intelligent word recognition, Artificial intelligence, Matching (statistics), Artificial neural network, Intelligent character recognition, Pattern recognition (psychology), Variation (astronomy), Graph, Distortion (music), Image (mathematics), Mathematics, Theoretical computer science, Statistics, Amplifier, Computer network, Geometry, Physics, Bandwidth (computing), AstrophysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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15Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.same | 26 |
| abstract_inverted_index.them | 85 |
| abstract_inverted_index.with | 122 |
| abstract_inverted_index.data, | 45 |
| abstract_inverted_index.every | 91 |
| abstract_inverted_index.graph | 89, 115 |
| abstract_inverted_index.image | 121 |
| abstract_inverted_index.order | 71 |
| abstract_inverted_index.paper | 32 |
| abstract_inverted_index.prove | 73 |
| abstract_inverted_index.shape | 27 |
| abstract_inverted_index.since | 15 |
| abstract_inverted_index.style | 20 |
| abstract_inverted_index.this, | 74 |
| abstract_inverted_index.graphs | 124 |
| abstract_inverted_index.images | 80 |
| abstract_inverted_index.nature | 35 |
| abstract_inverted_index.neural | 48 |
| abstract_inverted_index.parent | 106 |
| abstract_inverted_index.people | 17 |
| abstract_inverted_index.styles | 100, 104 |
| abstract_inverted_index.unique | 99 |
| abstract_inverted_index.Genetic | 65 |
| abstract_inverted_index.between | 102 |
| abstract_inverted_index.capable | 54 |
| abstract_inverted_index.graphs. | 87 |
| abstract_inverted_index.machine | 53 |
| abstract_inverted_index.mixing. | 127 |
| abstract_inverted_index.network | 49 |
| abstract_inverted_index.unknown | 119 |
| abstract_inverted_index.written | 58 |
| abstract_inverted_index.approach | 50 |
| abstract_inverted_index.generate | 96 |
| abstract_inverted_index.involved | 110 |
| abstract_inverted_index.matching | 112 |
| abstract_inverted_index.problem. | 69 |
| abstract_inverted_index.Character | 108 |
| abstract_inverted_index.character | 92, 120 |
| abstract_inverted_index.converted | 84 |
| abstract_inverted_index.different | 16, 19 |
| abstract_inverted_index.generated | 116, 125 |
| abstract_inverted_index.motivates | 61 |
| abstract_inverted_index.variation | 9 |
| abstract_inverted_index.Algorithms | 66 |
| abstract_inverted_index.Challenges | 0 |
| abstract_inverted_index.character. | 107 |
| abstract_inverted_index.characters | 3 |
| abstract_inverted_index.conversion | 39 |
| abstract_inverted_index.distortion | 11 |
| abstract_inverted_index.electronic | 44 |
| abstract_inverted_index.intermixed | 94 |
| abstract_inverted_index.characters, | 14, 38 |
| abstract_inverted_index.characters. | 30, 59, 82 |
| abstract_inverted_index.handwritten | 2, 13, 37, 41 |
| abstract_inverted_index.recognition | 4, 109 |
| abstract_inverted_index.recognizing | 56 |
| abstract_inverted_index.demonstrates | 33 |
| abstract_inverted_index.intermediate | 101 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.30154826 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |