Design of a Recognition System Automatic Vehicle License Plate through a Convolution Neural Network Article Swipe
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.5120/ijca2017915703
The present work is a study on the practical application of Learning process (Deep Learning) in the development of a system of Automatic recognition of vehicle license plates.These systems commonly referred to as ALPR (Automatic License Plate Recognition) -are able to recognize the content of vehicles from the images captured by a camera.The system proposed in this work is based on an image classifier developed through supervised learning techniques with convolution neural network.These networks are one of the most profound learning architectures and are specifically designed to solve artificial vision, such as pattern recognition and classification of images.This paper also examines basic processing techniques and Image segmentation -such as smoothing filters, contour detectionnecessary for the proposed system to be able to extract the contents of the license plates for further analysis and classification.This paper demonstrates the feasibility of an ALPR system based on a convolution neural network, noting the critical importance it has to design a network architecture and training data set appropriate to the problem to be solved.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.5120/ijca2017915703
- https://doi.org/10.5120/ijca2017915703
- OA Status
- bronze
- Cited By
- 2
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2769158567
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2769158567Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5120/ijca2017915703Digital Object Identifier
- Title
-
Design of a Recognition System Automatic Vehicle License Plate through a Convolution Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-11-15Full publication date if available
- Authors
-
Rajendra Prasath, K. P. Sudheer, Rahul BoadhList of authors in order
- Landing page
-
https://doi.org/10.5120/ijca2017915703Publisher landing page
- PDF URL
-
https://doi.org/10.5120/ijca2017915703Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5120/ijca2017915703Direct OA link when available
- Concepts
-
Computer science, License, Convolution (computer science), Artificial intelligence, Convolutional neural network, Artificial neural network, Speech recognition, Pattern recognition (psychology), Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2019: 1, 2018: 1Per-year citation counts (last 5 years)
- References (count)
-
10Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2769158567 |
|---|---|
| doi | https://doi.org/10.5120/ijca2017915703 |
| ids.doi | https://doi.org/10.5120/ijca2017915703 |
| ids.mag | 2769158567 |
| ids.openalex | https://openalex.org/W2769158567 |
| fwci | 0.50088856 |
| type | article |
| title | Design of a Recognition System Automatic Vehicle License Plate through a Convolution Neural Network |
| biblio.issue | 3 |
| biblio.volume | 177 |
| biblio.last_page | 54 |
| biblio.first_page | 47 |
| topics[0].id | https://openalex.org/T12707 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9929999709129333 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2214 |
| topics[0].subfield.display_name | Media Technology |
| topics[0].display_name | Vehicle License Plate Recognition |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.9110172986984253 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C2780560020 |
| concepts[1].level | 2 |
| concepts[1].score | 0.850516140460968 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q79719 |
| concepts[1].display_name | License |
| concepts[2].id | https://openalex.org/C45347329 |
| concepts[2].level | 3 |
| concepts[2].score | 0.7106163501739502 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q5166604 |
| concepts[2].display_name | Convolution (computer science) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5177389979362488 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C81363708 |
| concepts[4].level | 2 |
| concepts[4].score | 0.47789132595062256 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[4].display_name | Convolutional neural network |
| concepts[5].id | https://openalex.org/C50644808 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4696972370147705 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[5].display_name | Artificial neural network |
| concepts[6].id | https://openalex.org/C28490314 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3871912658214569 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q189436 |
| concepts[6].display_name | Speech recognition |
| concepts[7].id | https://openalex.org/C153180895 |
| concepts[7].level | 2 |
| concepts[7].score | 0.3752012848854065 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[7].display_name | Pattern recognition (psychology) |
| concepts[8].id | https://openalex.org/C111919701 |
| concepts[8].level | 1 |
| concepts[8].score | 0.1323724389076233 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[8].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.9110172986984253 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/license |
| keywords[1].score | 0.850516140460968 |
| keywords[1].display_name | License |
| keywords[2].id | https://openalex.org/keywords/convolution |
| keywords[2].score | 0.7106163501739502 |
| keywords[2].display_name | Convolution (computer science) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5177389979362488 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[4].score | 0.47789132595062256 |
| keywords[4].display_name | Convolutional neural network |
| keywords[5].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[5].score | 0.4696972370147705 |
| keywords[5].display_name | Artificial neural network |
| keywords[6].id | https://openalex.org/keywords/speech-recognition |
| keywords[6].score | 0.3871912658214569 |
| keywords[6].display_name | Speech recognition |
| keywords[7].id | https://openalex.org/keywords/pattern-recognition |
| keywords[7].score | 0.3752012848854065 |
| keywords[7].display_name | Pattern recognition (psychology) |
| keywords[8].id | https://openalex.org/keywords/operating-system |
| keywords[8].score | 0.1323724389076233 |
| keywords[8].display_name | Operating system |
| language | en |
| locations[0].id | doi:10.5120/ijca2017915703 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210206007 |
| locations[0].source.issn | 0975-8887 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0975-8887 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | International Journal of Computer Applications |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | https://doi.org/10.5120/ijca2017915703 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | International Journal of Computer Applications |
| locations[0].landing_page_url | http://doi.org/10.5120/ijca2017915703 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5066674084 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0826-847X |
| authorships[0].author.display_name | Rajendra Prasath |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Mathematics, Madanapalle Institute of Technology and Science, Madanapalle, India |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | P. Rajendra |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Mathematics, Madanapalle Institute of Technology and Science, Madanapalle, India |
| authorships[1].author.id | https://openalex.org/A5111846215 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | K. P. Sudheer |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210117495 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science & Engineering, KG Reddy College of Engineering & Technology, Hyderabad, India |
| authorships[1].institutions[0].id | https://openalex.org/I4210117495 |
| authorships[1].institutions[0].ror | https://ror.org/01rkxa860 |
| authorships[1].institutions[0].type | company |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210117495 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Dr. Reddy's Laboratories (India) |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | K. Sudheer |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science & Engineering, KG Reddy College of Engineering & Technology, Hyderabad, India |
| authorships[2].author.id | https://openalex.org/A5072040966 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8506-2088 |
| authorships[2].author.display_name | Rahul Boadh |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210114149 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Basic and Applied Sciences, K.R. Mangalam University, Gurgaon, India |
| authorships[2].institutions[0].id | https://openalex.org/I4210114149 |
| authorships[2].institutions[0].ror | https://ror.org/026b9sf88 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210114149 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | KR Mangalam University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Rahul Boadh |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Basic and Applied Sciences, K.R. Mangalam University, Gurgaon, India |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5120/ijca2017915703 |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Design of a Recognition System Automatic Vehicle License Plate through a Convolution Neural Network |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12707 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9929999709129333 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2214 |
| primary_topic.subfield.display_name | Media Technology |
| primary_topic.display_name | Vehicle License Plate Recognition |
| related_works | https://openalex.org/W2606446052, https://openalex.org/W2036021480, https://openalex.org/W3195777957, https://openalex.org/W2382668227, https://openalex.org/W2348482143, https://openalex.org/W2024584030, https://openalex.org/W2137048368, https://openalex.org/W1603675680, https://openalex.org/W2964954556, https://openalex.org/W3019910406 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2019 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2018 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.5120/ijca2017915703 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210206007 |
| best_oa_location.source.issn | 0975-8887 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0975-8887 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | International Journal of Computer Applications |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://doi.org/10.5120/ijca2017915703 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | International Journal of Computer Applications |
| best_oa_location.landing_page_url | http://doi.org/10.5120/ijca2017915703 |
| primary_location.id | doi:10.5120/ijca2017915703 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210206007 |
| primary_location.source.issn | 0975-8887 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0975-8887 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | International Journal of Computer Applications |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | https://doi.org/10.5120/ijca2017915703 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Journal of Computer Applications |
| primary_location.landing_page_url | http://doi.org/10.5120/ijca2017915703 |
| publication_date | 2017-11-15 |
| publication_year | 2017 |
| referenced_works | https://openalex.org/W2163605009, https://openalex.org/W2112796928, https://openalex.org/W2156163116, https://openalex.org/W2095705004, https://openalex.org/W2747397511, https://openalex.org/W2189472102, https://openalex.org/W2618530766, https://openalex.org/W2793045126, https://openalex.org/W4391811626, https://openalex.org/W4285719527 |
| referenced_works_count | 10 |
| abstract_inverted_index.a | 4, 19, 51, 143, 155 |
| abstract_inverted_index.an | 61, 138 |
| abstract_inverted_index.as | 32, 91, 108 |
| abstract_inverted_index.be | 118, 167 |
| abstract_inverted_index.by | 50 |
| abstract_inverted_index.in | 15, 55 |
| abstract_inverted_index.is | 3, 58 |
| abstract_inverted_index.it | 151 |
| abstract_inverted_index.of | 10, 18, 21, 24, 44, 76, 96, 124, 137 |
| abstract_inverted_index.on | 6, 60, 142 |
| abstract_inverted_index.to | 31, 40, 86, 117, 120, 153, 163, 166 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 82, 94, 104, 131, 158 |
| abstract_inverted_index.are | 74, 83 |
| abstract_inverted_index.for | 113, 128 |
| abstract_inverted_index.has | 152 |
| abstract_inverted_index.one | 75 |
| abstract_inverted_index.set | 161 |
| abstract_inverted_index.the | 7, 16, 42, 47, 77, 114, 122, 125, 135, 148, 164 |
| abstract_inverted_index.-are | 38 |
| abstract_inverted_index.ALPR | 33, 139 |
| abstract_inverted_index.able | 39, 119 |
| abstract_inverted_index.also | 99 |
| abstract_inverted_index.data | 160 |
| abstract_inverted_index.from | 46 |
| abstract_inverted_index.most | 78 |
| abstract_inverted_index.such | 90 |
| abstract_inverted_index.this | 56 |
| abstract_inverted_index.with | 69 |
| abstract_inverted_index.work | 2, 57 |
| abstract_inverted_index.(Deep | 13 |
| abstract_inverted_index.-such | 107 |
| abstract_inverted_index.Image | 105 |
| abstract_inverted_index.Plate | 36 |
| abstract_inverted_index.based | 59, 141 |
| abstract_inverted_index.basic | 101 |
| abstract_inverted_index.image | 62 |
| abstract_inverted_index.paper | 98, 133 |
| abstract_inverted_index.solve | 87 |
| abstract_inverted_index.study | 5 |
| abstract_inverted_index.design | 154 |
| abstract_inverted_index.images | 48 |
| abstract_inverted_index.neural | 71, 145 |
| abstract_inverted_index.noting | 147 |
| abstract_inverted_index.plates | 127 |
| abstract_inverted_index.system | 20, 53, 116, 140 |
| abstract_inverted_index.License | 35 |
| abstract_inverted_index.content | 43 |
| abstract_inverted_index.contour | 111 |
| abstract_inverted_index.extract | 121 |
| abstract_inverted_index.further | 129 |
| abstract_inverted_index.license | 26, 126 |
| abstract_inverted_index.network | 156 |
| abstract_inverted_index.pattern | 92 |
| abstract_inverted_index.present | 1 |
| abstract_inverted_index.problem | 165 |
| abstract_inverted_index.process | 12 |
| abstract_inverted_index.solved. | 168 |
| abstract_inverted_index.systems | 28 |
| abstract_inverted_index.through | 65 |
| abstract_inverted_index.vehicle | 25 |
| abstract_inverted_index.vision, | 89 |
| abstract_inverted_index.Learning | 11 |
| abstract_inverted_index.analysis | 130 |
| abstract_inverted_index.captured | 49 |
| abstract_inverted_index.commonly | 29 |
| abstract_inverted_index.contents | 123 |
| abstract_inverted_index.critical | 149 |
| abstract_inverted_index.designed | 85 |
| abstract_inverted_index.examines | 100 |
| abstract_inverted_index.filters, | 110 |
| abstract_inverted_index.learning | 67, 80 |
| abstract_inverted_index.network, | 146 |
| abstract_inverted_index.networks | 73 |
| abstract_inverted_index.profound | 79 |
| abstract_inverted_index.proposed | 54, 115 |
| abstract_inverted_index.referred | 30 |
| abstract_inverted_index.training | 159 |
| abstract_inverted_index.vehicles | 45 |
| abstract_inverted_index.Automatic | 22 |
| abstract_inverted_index.Learning) | 14 |
| abstract_inverted_index.developed | 64 |
| abstract_inverted_index.practical | 8 |
| abstract_inverted_index.recognize | 41 |
| abstract_inverted_index.smoothing | 109 |
| abstract_inverted_index.(Automatic | 34 |
| abstract_inverted_index.artificial | 88 |
| abstract_inverted_index.camera.The | 52 |
| abstract_inverted_index.classifier | 63 |
| abstract_inverted_index.importance | 150 |
| abstract_inverted_index.processing | 102 |
| abstract_inverted_index.supervised | 66 |
| abstract_inverted_index.techniques | 68, 103 |
| abstract_inverted_index.application | 9 |
| abstract_inverted_index.appropriate | 162 |
| abstract_inverted_index.convolution | 70, 144 |
| abstract_inverted_index.development | 17 |
| abstract_inverted_index.feasibility | 136 |
| abstract_inverted_index.images.This | 97 |
| abstract_inverted_index.recognition | 23, 93 |
| abstract_inverted_index.Recognition) | 37 |
| abstract_inverted_index.architecture | 157 |
| abstract_inverted_index.demonstrates | 134 |
| abstract_inverted_index.plates.These | 27 |
| abstract_inverted_index.segmentation | 106 |
| abstract_inverted_index.specifically | 84 |
| abstract_inverted_index.architectures | 81 |
| abstract_inverted_index.network.These | 72 |
| abstract_inverted_index.classification | 95 |
| abstract_inverted_index.detectionnecessary | 112 |
| abstract_inverted_index.classification.This | 132 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.71444548 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |