HANDWRITTEN NUMERAL RECOGNITION USING MULTI-TASK LEARNING

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • Jinhui Hou
  • Huanqiang Zeng
  • Lei Cai
  • Jianqing Zhu
  • Jiuwen Cao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) - Proceedings
PublisherIEEE
Pages155-158
ISBN (Electronic)9781538621592
ISBN (Print)9781538621608
Publication statusPublished - Nov 2017

Conference

Title25th IEEE International Symposium on Intelligent Signal Processing and Communication Systems 2017 (ISPACS 2017)
LocationWanda Realm Xiamen North Bay Hotel
PlaceChina
CityXiamen
Period6 - 9 November 2017

Abstract

Handwritten numeral recognition is a challenging problem due to large variation in the writing styles of different persons and high similarity in the contour of different digits. Based on the observation that the decision of scratchy/non-scratchy in the writing style could play a complementary role on the classification of handwritten numeral. In this paper, an effective multi-Task learning network for handwritten numeral recognition is proposed to enhance the recognition performance. The proposed multi-Task learning network consists of two tasks, which can simultaneously learn handwritten numeral recognition and the scratchy/non-scratchy decision. Furthermore, the two tasks can promote each other during training and achieve a better recognition performance. Extensive experiments on the MNIST database demonstrate that the proposed multi-Task network can effectively improve the recognition accuracy and achieve a superior performance of 0.40% error rate, which outperforms most methods that take experiments on the M-NIST database.

Research Area(s)

  • Convolutional neural network, Handwritten numeral recognition, MultiTask learning

Citation Format(s)

HANDWRITTEN NUMERAL RECOGNITION USING MULTI-TASK LEARNING. / Hou, Jinhui; Zeng, Huanqiang; Cai, Lei; Zhu, Jianqing; Cao, Jiuwen; Hou, Junhui.

2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) - Proceedings. IEEE, 2017. p. 155-158.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review