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Real-Time Multi-Digit Recognition System Using Deep Learning on an Embedded System

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Published:05 January 2018Publication History

ABSTRACT

In the machine learning technology, making computers read letters, characters and digits have been a hot issue in many areas of research. Among them, the recognition of handwritten numbers has still a long way to go, unlike the recognition of printed digits or the recognition of handwritten English sentences. In this paper we will introduce to you a multiple handwritten digit recognition system using deep learning.

References

  1. Haider A. Alwzwazy, Hayder M. Albehadili, Younes S. Alwan, and Naz E. Islam. 2016. Handwritten Digit Recognition Using Convolutional Neural Networks. IJIRCCE 4, 2 (Feb. 2016), 1101--1106.Google ScholarGoogle Scholar
  2. Sherif Abdel Azeem, Maha El Meseery, and Hany Ahmed. 2012. Online Arabic Handwritten Digits Recognition. In 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 135--140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning (POPL '79). ACM, New York, NY, USA, 160--167. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Chao Dong, Chen C. Loy, Kaiming He, and Xiaoou Tang. 2015. Image Super-Resolution Using Deep Convolutional Networks. TPAMI 38, 2 (June 2015). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Venu Govindaraju, Rohini Srihari, and Sargur Srihari. 1995. Handwritten text recognition. In Proceedings of IAPR Workshop on Document Analysis Systems. World Scientific Publishing, Singapore, 288--306.Google ScholarGoogle Scholar
  6. Yangqing Jia, Evan Shelhamer, and Jeff Donahue Sergey Karayev. 2014. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM international conference on Multimedia (MM '14). ACM, New York, NY, USA, 675--678. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Jin-Ho Kim and Duck-Soo Noh. 2012. Vehicle License Plate Recognition System By Edge-based Segment Image Generation. The Journal of the Korea Contents Association 12, 3 (March 2012), 9--16.Google ScholarGoogle Scholar
  8. Yann LeCun, Corinna Cortes, and Christopher J.C. Burges. {n. d.}. MNIST handwritten digit database, Yann LeCun, Corinna Cortes, and Chris Burges. ({n. d.}). Retrieved Nov. 30, 2017 from http://yann.lecun.com/exdb/mnist/Google ScholarGoogle Scholar
  9. Sangho Lee, Janghee Cho, and Da Young Ju. 2013. Autonomous Vehicle Simulation Project. IJSEIA 7, 5 (Sept. 2013), 393--402.Google ScholarGoogle ScholarCross RefCross Ref
  10. Samir Majumdar and Digvis S. Jayas. 2000. Classification of cereal grains using machine vision: IV. Combined morphology, color, and texture models. Transactions of the ASAE 43, 6 (2000).Google ScholarGoogle Scholar
  11. Ala Mhalla, Thierry chateau, Sami Gazzah, and Najoua E. B. Amara. 2016. A Faster R-CNN Multi-Object Detector on a Nvidia Jetson TX1 Embedded System: Demo. In Proceedings of the 10th International Conference on Distributed Smart Camera (ICDSC '16). ACM, New York, NY, USA, 208--209. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Yi Sun, Xiaogang Wang, and Xiaoou Tang. 2014. Deep Learning Face Representation by Joint Identification-Verification. (2014). arXiv:arXiv:1406.4773 Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Real-Time Multi-Digit Recognition System Using Deep Learning on an Embedded System

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    • Published in

      cover image ACM Other conferences
      IMCOM '18: Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication
      January 2018
      628 pages
      ISBN:9781450363853
      DOI:10.1145/3164541

      Copyright © 2018 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 5 January 2018

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      • research-article
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      • Refereed limited

      Acceptance Rates

      IMCOM '18 Paper Acceptance Rate100of255submissions,39%Overall Acceptance Rate213of621submissions,34%

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