Sequence-Information Recognition Method Based on Integrated mDTW
In the fields of machine learning and artificial intelligence, the processing of time-series data has been a continuous concern and a significant algorithm for intelligent applications. Traditional deep-learning-based methods seem to have reached performance ceilings in certain specific areas, such...
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MDPI AG
2024-09-01
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| Series: | Applied Sciences |
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| author | Boliang Sun Chao Chen |
| author_facet | Boliang Sun Chao Chen |
| author_sort | Boliang Sun |
| collection | DOAJ |
| description | In the fields of machine learning and artificial intelligence, the processing of time-series data has been a continuous concern and a significant algorithm for intelligent applications. Traditional deep-learning-based methods seem to have reached performance ceilings in certain specific areas, such as online character recognition. This paper proposes an algorithmic framework to break this deadlock by classifying time-series data by evaluating the similarities among handwriting samples using multidimensional Dynamic Time Warping (mDTW) distances. A simplified hierarchical clustering algorithm is employed as a classifier for character recognition. Moreover, this work achieves joint modeling with current mainstream temporal models, enabling the mDTW model to integrate modeling results from methods like RNN or Transformer, therefore further enhancing the accuracy of related algorithms. A series of experiments were conducted on a public database, and the results indicate that our method overcomes the bottleneck of current deep-learning-based methods in the field of online handwriting character recognition. More importantly, compared to deep -learning-based methods, the proposed method has a simpler structure and higher interpretability. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art models in handwriting character recognition, achieving a top-1 accuracy of 98.5% and a top-3 accuracy of 99.3%, thus confirming its effectiveness in overcoming the limitations of traditional deep-learning models in temporal sequence processing. |
| format | Article |
| id | doaj-art-3ddfdc1ae63748b2bb4ad70ee23799c3 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3ddfdc1ae63748b2bb4ad70ee23799c32025-08-20T01:47:41ZengMDPI AGApplied Sciences2076-34172024-09-011419871610.3390/app14198716Sequence-Information Recognition Method Based on Integrated mDTWBoliang Sun0Chao Chen1Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, ChinaLaboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, ChinaIn the fields of machine learning and artificial intelligence, the processing of time-series data has been a continuous concern and a significant algorithm for intelligent applications. Traditional deep-learning-based methods seem to have reached performance ceilings in certain specific areas, such as online character recognition. This paper proposes an algorithmic framework to break this deadlock by classifying time-series data by evaluating the similarities among handwriting samples using multidimensional Dynamic Time Warping (mDTW) distances. A simplified hierarchical clustering algorithm is employed as a classifier for character recognition. Moreover, this work achieves joint modeling with current mainstream temporal models, enabling the mDTW model to integrate modeling results from methods like RNN or Transformer, therefore further enhancing the accuracy of related algorithms. A series of experiments were conducted on a public database, and the results indicate that our method overcomes the bottleneck of current deep-learning-based methods in the field of online handwriting character recognition. More importantly, compared to deep -learning-based methods, the proposed method has a simpler structure and higher interpretability. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art models in handwriting character recognition, achieving a top-1 accuracy of 98.5% and a top-3 accuracy of 99.3%, thus confirming its effectiveness in overcoming the limitations of traditional deep-learning models in temporal sequence processing.https://www.mdpi.com/2076-3417/14/19/8716computational intelligencemultidimensional dynamic time warping (mDTW)online character recognitiondeep-learning model integration |
| spellingShingle | Boliang Sun Chao Chen Sequence-Information Recognition Method Based on Integrated mDTW Applied Sciences computational intelligence multidimensional dynamic time warping (mDTW) online character recognition deep-learning model integration |
| title | Sequence-Information Recognition Method Based on Integrated mDTW |
| title_full | Sequence-Information Recognition Method Based on Integrated mDTW |
| title_fullStr | Sequence-Information Recognition Method Based on Integrated mDTW |
| title_full_unstemmed | Sequence-Information Recognition Method Based on Integrated mDTW |
| title_short | Sequence-Information Recognition Method Based on Integrated mDTW |
| title_sort | sequence information recognition method based on integrated mdtw |
| topic | computational intelligence multidimensional dynamic time warping (mDTW) online character recognition deep-learning model integration |
| url | https://www.mdpi.com/2076-3417/14/19/8716 |
| work_keys_str_mv | AT boliangsun sequenceinformationrecognitionmethodbasedonintegratedmdtw AT chaochen sequenceinformationrecognitionmethodbasedonintegratedmdtw |