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.

本篇综述研究了手写文字光学字符识别技术及其研究方向，通过人工智能和机器学习工具将手写和印刷文件转化为可分析、可编辑和可搜索的电子数据。共收集 142 篇相关研究，总结了最新的 OCR 技术和研究方向。