Enhancing Handwritten Digit Recognition using Auxiliary Classifier Generative Adversarial Networks and Self-attention Mechanism
This research study investigates the integration of the self-attention mechanism and the Auxiliary Classifier Generative Adversarial Networks (ACGAN)to improve handwritten digital recognition using the MNIST data set. Although progression has been made in the generative Adversarial Networks (GANs) u...
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| 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_03016.pdf |
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| _version_ | 1825206548432945152 |
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| author | Hu Tingkai |
| author_facet | Hu Tingkai |
| author_sort | Hu Tingkai |
| collection | DOAJ |
| description | This research study investigates the integration of the self-attention mechanism and the Auxiliary Classifier Generative Adversarial Networks (ACGAN)to improve handwritten digital recognition using the MNIST data set. Although progression has been made in the generative Adversarial Networks (GANs) used for image synthesis, it is still tested to attain top-notch, context-accurate photo generation, specifically under various information problems. This study fixes these spaces by incorporating self-attention with ACGANs, improving the integrity and labelling accuracy of the produced images. This approach entails changing the ACGAN framework to incorporate the self-focus module, so far better identify the context dependencies in the photo. This assimilation advertises much more detailed and accurate numerical depiction, which is especially helpful when converting data sets with dark histories right into more clear, segmented white background pictures, which enhances the differentiation between data classes. The outcomes reveal that the recognition accuracy and processing speed have been significantly boosted, validating the adaptability and usefulness of the design in various procedure circumstances. The research results show that this method can establish brand-new standards for producing high-grade electronic photos in numerous applications and show considerable progress in the field of image recognition technology. |
| format | Article |
| id | doaj-art-2335c0d6653b4ef4b03170e02f635e11 |
| 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-2335c0d6653b4ef4b03170e02f635e112025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700301610.1051/itmconf/20257003016itmconf_dai2024_03016Enhancing Handwritten Digit Recognition using Auxiliary Classifier Generative Adversarial Networks and Self-attention MechanismHu Tingkai0Stony Brook University, Anhui UniversityThis research study investigates the integration of the self-attention mechanism and the Auxiliary Classifier Generative Adversarial Networks (ACGAN)to improve handwritten digital recognition using the MNIST data set. Although progression has been made in the generative Adversarial Networks (GANs) used for image synthesis, it is still tested to attain top-notch, context-accurate photo generation, specifically under various information problems. This study fixes these spaces by incorporating self-attention with ACGANs, improving the integrity and labelling accuracy of the produced images. This approach entails changing the ACGAN framework to incorporate the self-focus module, so far better identify the context dependencies in the photo. This assimilation advertises much more detailed and accurate numerical depiction, which is especially helpful when converting data sets with dark histories right into more clear, segmented white background pictures, which enhances the differentiation between data classes. The outcomes reveal that the recognition accuracy and processing speed have been significantly boosted, validating the adaptability and usefulness of the design in various procedure circumstances. The research results show that this method can establish brand-new standards for producing high-grade electronic photos in numerous applications and show considerable progress in the field of image recognition technology.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03016.pdf |
| spellingShingle | Hu Tingkai Enhancing Handwritten Digit Recognition using Auxiliary Classifier Generative Adversarial Networks and Self-attention Mechanism ITM Web of Conferences |
| title | Enhancing Handwritten Digit Recognition using Auxiliary Classifier Generative Adversarial Networks and Self-attention Mechanism |
| title_full | Enhancing Handwritten Digit Recognition using Auxiliary Classifier Generative Adversarial Networks and Self-attention Mechanism |
| title_fullStr | Enhancing Handwritten Digit Recognition using Auxiliary Classifier Generative Adversarial Networks and Self-attention Mechanism |
| title_full_unstemmed | Enhancing Handwritten Digit Recognition using Auxiliary Classifier Generative Adversarial Networks and Self-attention Mechanism |
| title_short | Enhancing Handwritten Digit Recognition using Auxiliary Classifier Generative Adversarial Networks and Self-attention Mechanism |
| title_sort | enhancing handwritten digit recognition using auxiliary classifier generative adversarial networks and self attention mechanism |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03016.pdf |
| work_keys_str_mv | AT hutingkai enhancinghandwrittendigitrecognitionusingauxiliaryclassifiergenerativeadversarialnetworksandselfattentionmechanism |