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Handwritten Character Recognition Using Convolutional Neural Networks

  • Unique Paper ID: 163171
  • Volume: 10
  • Issue: 11
  • PageNo: 962-969
  • Abstract:
  • As handwritten documents are so common in human transaction optical character recognition (OCR) has enormous practical value. The science of optical character recognition (OCR) makes it possible to convert different kinds of documents and photos into data that can be searched, edited, and analysed. In the last ten years, academics have utilised machine learning and artificial intelligence techniques more often to automatically scan paper documents and handwritten ones, converting them into electronic representations. This review article's objectives are to offer a thorough overview of the studies done on character identification in handwritten documents and to recommend future lines of inquiry. We methodically gathered, synthesised, and examined research publications on handwritten OCR and closely related subjects published between 2000 and 2019 for this Systematic Literature Review (SLR). We conducted our reviews in accordance with generally accepted procedures, and we used electronic databases to find pertinent publications. To make sure that all publications pertinent to the subject were found, our search strategy comprised both forward and backward reference searches in addition to the use of targeted keywords. Following an extensive process of study selection, we found and examined 176 publications in total for this SLR. This review article seeks to accomplish two goals: first, it presents the most recent findings and methods in the field of optical character recognition (OCR), highlighting the advancements made in the previous 20 years. Second, by identifying places where there are gaps in the current body of knowledge that need to be filled, it aims to direct future study.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 11
  • PageNo: 962-969

Handwritten Character Recognition Using Convolutional Neural Networks

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