Paper Info
Title
Adversarial Feature Enhancing Network for End-to-End Handwritten Paragraph Recognition
Abstract
To date, offline handwriting paragraph recognition systems either separately crop text line images and recognize them or perform implicit line segmentation by integrating complicated multi-dimensional long short-term memory (MDLSTM) with an attention mechanism. The former abovementioned approachs could lead to sub-optimal performances while the latter is very time-consuming. In this paper, a fast end-to-end system, called adversarial feature enhancing network (AFEN), is proposed for offline handwritten paragraph recognition. The proposed AFEN system comprises five components: a shared feature extractor for robust feature learning, a text detection branch for text box proposal; RoIRotate for oriented feature region extraction, an adversarial feature learning network for joint feature learning of text detection and recognition branch, and a text recognition branch for text transcription. Experiments on two popular handwritten paragraph recognition benchmarks, namely IAM and Rimes are used to verify the efficacy of the proposed AFEN system. The proposed approach yields impressive results compared to previously proposed systems in the literature.
Year
DOI
Venue
2019
10.1109/ICDAR.2019.00073
2019 International Conference on Document Analysis and Recognition (ICDAR)
Keywords
Field
DocType
Adversarial Learning,Handwritten text recognition,Text Detection,Paragraph Recognition
Pattern recognition,Handwriting,Computer science,End-to-end principle,Segmentation,Text box,Speech recognition,Paragraph,Artificial intelligence,Feature learning,Text detection,Adversarial system
Conference
ISSN
ISBN
Citations 
1520-5363
978-1-7281-3015-6
0
PageRank 
References 
Authors
0.34
11
5
Name
Order
Citations
PageRank
Yao-Xiong Huang123.45
zecheng xie2967.55
Lianwen Jin31337113.14
Yuanzhi Zhu4162.91
Shuaitao Zhang5303.86