Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches
This paper provides a comprehensive review of character recognition technologies, focusing on the application of Convolutional Neural Networks (CNN) and deep learning methodologies. Through an analysis of three key studies, the research highlights the strengths and limitations of current approaches....
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03010.pdf |
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| _version_ | 1825206551022927872 |
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| author | Yang Ximin |
| author_facet | Yang Ximin |
| author_sort | Yang Ximin |
| collection | DOAJ |
| description | This paper provides a comprehensive review of character recognition technologies, focusing on the application of Convolutional Neural Networks (CNN) and deep learning methodologies. Through an analysis of three key studies, the research highlights the strengths and limitations of current approaches. Study by Zib emphasizes the challenges in segmenting and recognizing English characters using CNN, revealing the need for supplementary techniques to mitigate errors. Research by Nikitha explores the impact of increasing the dimensionality of analysis, demonstrating that higher dimensions improve accuracy but also extend training times. Similarly, work conducted by Pradeep shows that larger vector sizes enhance recognition accuracy but at the cost of greater computational resources. The collective findings suggest that while CNN and deep learning models have significantly advanced character recognition, there remains a need for enhanced segmentation techniques and a balanced approach to optimizing training efficiency and accuracy. Future research should focus on integrating supportive methods to improve segmentation and finding an optimal trade-off between variable complexity and computational efficiency, thereby advancing the practical application of character recognition systems across various domains. |
| format | Article |
| id | doaj-art-2ff3010ef51c420fbe84cdfcd2a1571e |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-2ff3010ef51c420fbe84cdfcd2a1571e2025-02-07T08:21:12ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700301010.1051/itmconf/20257003010itmconf_dai2024_03010Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning ApproachesYang Ximin0Graduate School of Arts & Science, Shanghai New York UniversityThis paper provides a comprehensive review of character recognition technologies, focusing on the application of Convolutional Neural Networks (CNN) and deep learning methodologies. Through an analysis of three key studies, the research highlights the strengths and limitations of current approaches. Study by Zib emphasizes the challenges in segmenting and recognizing English characters using CNN, revealing the need for supplementary techniques to mitigate errors. Research by Nikitha explores the impact of increasing the dimensionality of analysis, demonstrating that higher dimensions improve accuracy but also extend training times. Similarly, work conducted by Pradeep shows that larger vector sizes enhance recognition accuracy but at the cost of greater computational resources. The collective findings suggest that while CNN and deep learning models have significantly advanced character recognition, there remains a need for enhanced segmentation techniques and a balanced approach to optimizing training efficiency and accuracy. Future research should focus on integrating supportive methods to improve segmentation and finding an optimal trade-off between variable complexity and computational efficiency, thereby advancing the practical application of character recognition systems across various domains.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03010.pdf |
| spellingShingle | Yang Ximin Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches ITM Web of Conferences |
| title | Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches |
| title_full | Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches |
| title_fullStr | Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches |
| title_full_unstemmed | Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches |
| title_short | Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches |
| title_sort | advancements and challenges in character recognition a comparative analysis of cnn and deep learning approaches |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03010.pdf |
| work_keys_str_mv | AT yangximin advancementsandchallengesincharacterrecognitionacomparativeanalysisofcnnanddeeplearningapproaches |