Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

• URL: http://arxiv.org/abs/2001.00139v1
• Date: Wed, 1 Jan 2020 04:55:04 GMT
• Title: Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)
• Authors: Jamshed Memon, Maira Sami, Rizwan Ahmed Khan
• Abstract summary: This review article serves the purpose of presenting state of the art results and techniques on OCR. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2018. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 142 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

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