Improved Handwritten Numeral Recognition on MNIST Dataset with YOLO and LSTM

Yalin Wen, Wei Ke, Hao Sheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the aging of population and the advance of technology, handwritten numeral recognition system is sophisticated and widely used. However, due to the presence of different writing surfaces, postures and other factors, the performance of handwritten numeral recognition is limited. In this paper, we propose a new supervised recurrent neural network, which combines time and space for target location prediction on handwritten datasets. Our method is based on the YOLO framework, and combines a long and short term memory (LSTM) mechanism. Moreover, our method not only locates handwritten images, but also improves the classification accuracy. Extensive comparison with the state-of-the-art methods demonstrates that our method achieves both accuracy and robustness on handwritten datasets. Meanwhile, our method is effective with low computational cost.

Original languageEnglish
Title of host publication6th IEEE International Conference on Universal Village, UV 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665474771
DOIs
Publication statusPublished - 2022
Event6th IEEE International Conference on Universal Village, UV 2022 - Hybrid, Boston, United States
Duration: 22 Oct 202225 Oct 2022

Publication series

Name6th IEEE International Conference on Universal Village, UV 2022

Conference

Conference6th IEEE International Conference on Universal Village, UV 2022
Country/TerritoryUnited States
CityHybrid, Boston
Period22/10/2225/10/22

Keywords

  • handwritten numeral of recognition
  • tstm
  • yoto

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