Improving Deep Learning based Optical Character Recognition via Neural Architecture Search

Zhenyao Zhao, Min Jiang, Shihui Guo, Zhenzhong Wang, Fei Chao, Kay Chen Tan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)


Optical character rcecognition (OCR) is a process of converting images of typed, handwritten or printed text into machine-encoded one. In recent years, the methods represented by deep learning have greatly improved the performance of OCR systems, but the main challenges of such systems are 1) to accurately perform text detection in complex scenes and 2) to identify and set the optimal parameters to optimize the performance of the system. In this paper, we propose an OCR method based on Neural Architecture Search technique, called AutOCR. The characteristic of the proposed method is the automatic design of text detection framework using an evolutionary computation neural architecture search method. This design can not only accurately recognize the text in a complex environment, but also avoid the process of experts participating in parameter adjustment. We compared it with different methods, and the experimental results proved the effectiveness of our method.

Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169293
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

Name2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings


Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow

ASJC Scopus subject areas

  • Control and Optimization
  • Decision Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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