Open AccessOffline Handwritten Text Recognition Using Deep Learning: A Review
Author(s) -
Yintong Wang,
Wenjie Xiao,
Shuo Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012015
Subject(s) - computer science , handwriting , artificial intelligence , field (mathematics) , deep learning , handwriting recognition , text recognition , speech recognition , pattern recognition (psychology) , natural language processing , machine learning , image (mathematics) , feature extraction , mathematics , pure mathematics
The area of offline handwritten text recognition(OHTR) has been widely researched in the last decades, but it stills an important research problem. The OHTR system has an objective to transform a document image into text data. Compared with online handwriting recognition, the dynamic information about the writing trajectories in OHTR is not available. Many advancements have been proposed in the literature, most notably the application of deep learning methods to OHTR. In this paper, we introduced how this problem has been handled in the past few decades, analyze the latest advancements and the potential directions for future research in this field.