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.
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Chao Dong, Chen C. Loy, Kaiming He, and Xiaoou Tang. 2015. Image Super-Resolution Using Deep Convolutional Networks. TPAMI 38, 2 (June 2015). Google ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- Sangho Lee, Janghee Cho, and Da Young Ju. 2013. Autonomous Vehicle Simulation Project. IJSEIA 7, 5 (Sept. 2013), 393--402.Google ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- Yi Sun, Xiaogang Wang, and Xiaoou Tang. 2014. Deep Learning Face Representation by Joint Identification-Verification. (2014). arXiv:arXiv:1406.4773 Google ScholarDigital Library
Index Terms
- Real-Time Multi-Digit Recognition System Using Deep Learning on an Embedded System
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