TriView-ParNet: parallel network for hybrid recognition of touching printed and handwritten strings based on feature fusion and three-view co-training-Reference-Cited by-同舟云学术

TriView-ParNet: parallel network for hybrid recognition of touching printed and handwritten strings based on feature fusion and three-view co-training

Published:2022-12-21 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Qiu Junhao,Lai Shangyu,Huang Guoheng^ORCID,Zhang Weiwen,Mai Junhui,Pun Chi-Man,Ling Wing-Kuen

Funder

R&D projects in key areas of Guangdong Province

National Natural Science Foundation of China

Guangdong Key Areas R&D Program Project

Guangzhou Science and Technology Plan Project

Top Youth Talent Project of Zhujiang Talent Program

Guangdong Provincial Key Laboratory of Prevention and Control for Severe Clinical Animal Diseases

Science and technology research in key areas in Foshan

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-04257-x.pdf

Reference55 articles.

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3. Gattal A, Chibani Y (2015) Svm-based segmentation-verification of handwritten connected digits using the oriented sliding window. Int J Comput Intell Appl 14(01):1550005

4. Chen C, Guo J (2017) A general approach for handwritten digits segmentation using spectral clustering. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 01, pp 547–552

5. Chakraborty A, De R, Malakar S, Schwenker F, Sarkar R (2021) Handwritten digit string recognition using deep autoencoder based segmentation and resnet based recognition approach. In: 2020 25Th international conference on pattern recognition (ICPR), pp 7737–7742

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