数据与计算发展前沿 ›› 2019, Vol. 1 ›› Issue (2): 17-25.doi: 10.11871/jfdc.issn.2096-742X.2019.02.002

所属专题: “人工智能”专刊

• 人工智能专刊 • 上一篇    下一篇

文档图像识别技术回顾与展望

刘成林1,2,3,*()   

  1. 1. 中国科学院自动化研究所,模式识别国家重点实验室,北京 100190
    2. 中国科学院大学,人工智能学院,北京 100049
    3. 中国科学院脑科学与智能技术卓越创新中心,北京 100190
  • 收稿日期:2019-11-07 出版日期:2019-12-20 发布日期:2020-01-15
  • 通讯作者: 刘成林 E-mail:liucl@nlpr.ia.ac.cn
  • 作者简介:刘成林,1967年生,中国科学院自动化研究所,副所长,模式识别国家重点实验室主任,研究员、博士生导师。1989年毕业于武汉大学无线电信息工程系,1992年在北京工业大学获电路与系统专业工学硕士学位,1995年在中国科学院自动化研究所获模式识别与智能控制专业工学博士学位。1996年3月-1997年10月在韩国科学技术院(KAIST)从事博士后研究。1997年11月-1999年3月在日本东京农工大学从事博士后研究。1999年3月-2004年12月在日立中央研究所(东京)先后任研究员和主任研究员。2005年入选中国科学院“百人计划”。2008年获得国家杰出青年科学基金资助。研究兴趣包括图像处理、模式识别、机器学习、文字识别与文档分析等。在国内外期刊和学术会议上发表论文300余篇,合著英文专著一本。现任国际刊物Pattern Recognition的副主编,Image and Vision Computing, Int. J. Document Analysis and Recognition, Cognitive Computation的编委,国内期刊《自动化学报》的副主编。美国电气电子工程师协会会士 (IEEE Fellow)、国际模式识别学会会士(IAPR Fellow)。
    Liu Chenglin is a Professor at the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, and he is now the director of the laboratory. He received the B.S. degree in electronic engineering from Wuhan University, the M.E. degree in electronic engineering from Beijing Polytechnic University, and the Ph.D. degree in pattern recognition and intelligent control from the Chinese Academy of Sciences, in 1989, 1992 and 1995, respectively. He was a postdoctoral fellow at Korea Advanced Institute of Science and Technology (KAIST) and later at Tokyo University of Agriculture and Technology from March 1996 to March 1999. From 1999 to 2004, he was a research staff member and later a senior researcher at the Central Research Laboratory, Hitachi, Ltd., Tokyo, Japan. His research interests include pattern recognition, image processing, neural networks, machine learning, and especially the applications to character recognition and document analysis. He has published over 300 technical papers in journals and conferences. He won the IAPR/ICDAR Young Investigator Award of 2005. He is an associate editor-in-chief of Pattern Recognition Journal, an associate editor of Image and Vision and Computing, International Journal on Document Analysis and Recognition, and Cognitive Computation. He is a Fellow of the IAPR and the IEEE.
  • 基金资助:
    国家自然科学基金(61721004)

Document Image Recognition: Retrospective and Perspective of Technology

Liu Chenglin1,2,3,*()   

  1. 1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
    3. CAS Center for Excellence of Brain Science and Intelligence Technology, Beijing 100190, China
  • Received:2019-11-07 Online:2019-12-20 Published:2020-01-15
  • Contact: Liu Chenglin E-mail:liucl@nlpr.ia.ac.cn

摘要:

【目的】文档图像是一类广泛存在且具有重要应用价值的数据。从文档图像中检测文字并转化为计算机内码(电子文本)是文档识别的主要目标。自上世纪50年代以来,文档识别(又称文字识别,OCR)的研究和应用取得了巨大的进展。本文为科研人员和工程人员提供一个比较全面的文档图像识别技术总体介绍,便于大家开展技术创新和技术应用。【方法】本文在介绍文档识别应用背景的基础上,对该领域历史上主要方法进行回顾,对当前技术状况和研究动态进行分析,并展望未来发展趋势。【结果】1950年代到2000年代,在统计模式识别、特征提取、结构分析、字符切分、字符串识别和版面分析等方面积累了大量有效方法。【结论】近年来深度学习(深度神经网络)逐渐成为主导性的方法,使文字检测和识别的性能得到明显提升,但在复杂版面分析能力、文字识别的可靠性、泛化性等方面仍然存在不足。

关键词: 文档识别, 版面分析, 文本检测, 深度学习, 字符识别, 文本行识别

Abstract:

[Objective] Document images carry important information of texts which are extensive in daily life. Extracting texts from images and converting to digital texts to be processed by computers is the main objective of document image recognition (also called as character recognition or OCR). Since 1950s, the field of document recognition has seen tremendous advances in research and applications. This paper provides an overview of document image recognition, facilitating research innovations and engineering applications. [Methods] In this article, I first introduce the applications needs of document recognition, then review the main advances of research in this field, analyze the strengths and weaknesses of the methods, and finally, prospect the future development. [Results] Numerous methods of statistical recognition, feature extraction, structural analysis, character segmentation, character string recognition and layout analysis were proposed during 1950s-2000s. [Conclusions] In recent years, deep learning methods (deep neural networks, DNNs) dominate the field, and have promoted the performance of text detection and recognition significantly. However, insufficiencies are still evident in complex layout analysis, character recognition reliability and generalization.

Key words: document recognition, layout analysis, text detection, deep learning, character recognition, text line recognition