Investigation of Machine Learning Algorithms for Pattern Recognition in Image Processing
In order to recognize patterns in images, this study tests the performance of many "machine learning algorithms" and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ("deep learning, supp...
Saved in:
| Published in: | 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA) pp. 898 - 904 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
03.08.2023
|
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In order to recognize patterns in images, this study tests the performance of many "machine learning algorithms" and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ("deep learning, support vector machines, decision trees, and random forests") and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its "accuracy, precision, recall, and F1 score", among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms. |
|---|---|
| AbstractList | In order to recognize patterns in images, this study tests the performance of many "machine learning algorithms" and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ("deep learning, support vector machines, decision trees, and random forests") and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its "accuracy, precision, recall, and F1 score", among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms. |
| Author | Kate, Chennaiah Sharma, Arvind Kalpana, C. Kumar, S. Sandeep Kumar, Ashok Yadav, Ajay Singh |
| Author_xml | – sequence: 1 givenname: Chennaiah surname: Kate fullname: Kate, Chennaiah email: chennaiahkate@gmail.com organization: St. Peter's Engineering College,Department of Information Technology,Hyderabad,Telangana,India,500100 – sequence: 2 givenname: C. surname: Kalpana fullname: Kalpana, C. email: kalpanasoundar13@gmail.com organization: NPR College of Engineering & Technology,Department of Computer Science and Engineering,Dindigul,Tamil Nadu,India,624401 – sequence: 3 givenname: Arvind surname: Sharma fullname: Sharma, Arvind email: arvindsharma@gweca.ac.in organization: Government Women Engineering College,Department of Electronics and Communication Engineering,Ajmer,Rajasthan,India,305002 – sequence: 4 givenname: Ajay Singh surname: Yadav fullname: Yadav, Ajay Singh email: ajaysiny@srmist.edu.in organization: SRM Institute of Science and Technology,Department of Mathematics,Ghaziabad,Uttar Pradesh,India,201204 – sequence: 5 givenname: Ashok surname: Kumar fullname: Kumar, Ashok email: kuashok@banasthali.in organization: BanasthaliVidyapith,Department of Computer Science,Rajasthan,India,304022 – sequence: 6 givenname: S. Sandeep surname: Kumar fullname: Kumar, S. Sandeep email: ssandeep794@kluniversity.in organization: Koneru Lakshmaiah Education Foundation,Department of Computer Science and Engineering,Andhra Pradesh,India,522502 |
| BookMark | eNo1j8tKxDAYhSPoQsd5AxfxAVpzaZJmWYqXQsVhUHA3pOmfTmCaSBoE397iZXXg43wHzhU6DzEAQreUlJQSfde13b5thNI1KRlhvKSEMSKFPENbvVIuCGe0YuISvXfhE5bsJ5N9DDg6_Gzs0QfAPZgUfJhwc5pi8vk4L9jFhHcmZ0gB78HGKfgfzQfczWYCvEvRwrKs2jW6cOa0wPYvN-jt4f61fSr6l8eubfrCU6pzIZzmgxiHeuSq0tYoxagVkgyq1k4JZjkbtFVs7bCxdlJX1FYaVk1KI8jIN-jmd9cDwOEj-dmkr8P_Yf4Nej9RQg |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICIRCA57980.2023.10220656 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Forestry |
| EISBN | 9798350321425 |
| EndPage | 904 |
| ExternalDocumentID | 10220656 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i119t-5f93b5db8d3749ca7721c560b789f752c32b9c72b5d2d8f6941c49e5f966a50d3 |
| IEDL.DBID | RIE |
| IngestDate | Thu Jan 18 11:14:25 EST 2024 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i119t-5f93b5db8d3749ca7721c560b789f752c32b9c72b5d2d8f6941c49e5f966a50d3 |
| PageCount | 7 |
| ParticipantIDs | ieee_primary_10220656 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-Aug.-3 |
| PublicationDateYYYYMMDD | 2023-08-03 |
| PublicationDate_xml | – month: 08 year: 2023 text: 2023-Aug.-3 day: 03 |
| PublicationDecade | 2020 |
| PublicationTitle | 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA) |
| PublicationTitleAbbrev | ICIRCA |
| PublicationYear | 2023 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.862279 |
| Snippet | In order to recognize patterns in images, this study tests the performance of many "machine learning algorithms" and feature extraction methods. Here,... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 898 |
| SubjectTerms | "decision trees" "random forests" "support vector machines" computer vision Deep learning evaluation metrics feature extraction Forestry Handwriting recognition Histogram of Oriented Gradients Histograms image processing Image recognition Machine learning Machine learning algorithms pattern recognition raw pixel values synthetic images transfer learning |
| Title | Investigation of Machine Learning Algorithms for Pattern Recognition in Image Processing |
| URI | https://ieeexplore.ieee.org/document/10220656 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1ba8IwFD5MGbKn3Ry7k8Feo7ZNc3kUmUzYRGQbvkmbJk6Y7dC637-TWHd52MPeSsihkNB856Tn-z6A2zhFTOFW0DRQljKWKoolWEwVx-RYWRsknl_x8iCGQzmZqFFFVvdcGGOMbz4zLffo_-VnhV67q7K2q04QMnkNakLwDVmrATeVbmZ70BuMe91YKNlpOVfw1nb-L-cUDxz9_X--8gCa3xQ8MvoCl0PYMfkRNJyRpnNnO4bJD4GMIieFJY--LdKQSjF1RrpvswJL_9fFimBmSkZeSTMn423LEIbNczJY4IlCKr4AhjXhuX_31LunlUsCnQeBKmlsVZTGWSqzSDClE0yXA415TCqksiIOdRSmSosQ54SZtI64qpkyGMZ5Eney6ATqeZGbUyCZ-6CN4YlhGpFbSsuYiDiezQHWbZKdQdOt0PR9I4Qx3S7O-R_jF7Dn9sH3y0WXUC-Xa3MFu_qjnK-W1377PgHTPpwo |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bT8IwFD5RNOiTN4x3a-LrkG3t2j4SImERCCFoeCOsa5FENsPF3-9pGV4efPBtaXbSpE37nbOd7_sA7lmCmBIZ7iW-NB6lifSwBGOejDA5lsb4Y8eveGnzblcMh7JXkNUdF0Zr7ZrPdNU-un_5aa5W9lPZg61OEDKjbdhhlAa1NV2rDHeFcuZD3Ij7jTrjUtSq1he8uon45Z3ioKN58M9JD6HyTcIjvS94OYItnR1D2VppWn-2Exj-kMjIM5Ib0nGNkZoUmqkTUn-b5Fj8v84WBHNT0nNamhnpb5qGMGyakXiGdwopGAMYVoHn5uOg0fIKnwRv6vty6TEjw4SliUhDTqUaY8LsK8xkEi6k4SxQYZBIxQN8J0iFsdRVRaXGsCgas1oankIpyzN9BiS1R1rraKypQuwWwlDKwwhvZx8rN0HPoWJXaPS-lsIYbRbn4o_xW9hrDTrtUTvuPl3Cvt0T1z0XXkFpOV_pa9hVH8vpYn7jtvITIQCfbw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2023+5th+International+Conference+on+Inventive+Research+in+Computing+Applications+%28ICIRCA%29&rft.atitle=Investigation+of+Machine+Learning+Algorithms+for+Pattern+Recognition+in+Image+Processing&rft.au=Kate%2C+Chennaiah&rft.au=Kalpana%2C+C.&rft.au=Sharma%2C+Arvind&rft.au=Yadav%2C+Ajay+Singh&rft.date=2023-08-03&rft.pub=IEEE&rft.spage=898&rft.epage=904&rft_id=info:doi/10.1109%2FICIRCA57980.2023.10220656&rft.externalDocID=10220656 |