Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Fei Yin"'
Publikováno v:
ACM Multimedia
One of the intelligent transportation system's critical tasks is to understand traffic signs and convey traffic information to humans. However, most related works are focused on the detection and recognition of traffic sign texts or symbols, which is
Publikováno v:
Document Analysis and Recognition – ICDAR 2021 ISBN: 9783030863364
ICDAR (4)
ICDAR (4)
It happens in handwritten documents that text lines distort beyond sequential structure because of in-writing editions such as insertion and swapping of text. This kind of irregularity can not be handled using existing text line recognition methods t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97fafd37fc90477225a9a8a621a0fc06
https://doi.org/10.1007/978-3-030-86337-1_37
https://doi.org/10.1007/978-3-030-86337-1_37
Autor:
Cheng-Lin Liu, Yu-Pei Yan, Gui-Yun Chen, Da-Han Wang, Jin-Wen Wu, Fei Yin, Zhi Cai Huang, Yao Wang
Publikováno v:
ICFHR
This paper presents the competition on Offline Recognition and Spotting of Handwritten Mathematical Expressions (OffRaSHME) held at the 17th International Conference on Frontiers in Handwriting Recognition (ICFHR 2020). Handwritten Mathematical Expre
Autor:
Nibal Nayef, Yuan Feng, Cheng-Lin Liu, Fei Yin, Umapada Pal, Joseph Chazalon, Zhenbo Luo, Dimosthenis Karatzas, Muhammad Muzzamil Luqman, Christophe Rigaud, Imen Bizid, Jean-Marc Ogier, Jean-Christophe Burie, Wafa Khlif, Hyun-Soo Choi
Publikováno v:
ICDAR
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Nov 2017, Kyoto, Japan. pp.1454-1459, ⟨10.1109/ICDAR.2017.237⟩
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Nov 2017, Kyoto, Japan. pp.1454-1459, ⟨10.1109/ICDAR.2017.237⟩
Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to det
Publikováno v:
ICDAR
In handwriting recognition, the test data usually come from multiple writers which are not shown in the training data. Therefore, adapting the base classifier towards the new style of each writer can significantly improve the generalization performan
Publikováno v:
IEEE transactions on pattern analysis and machine intelligence. 40(4)
Recent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world. Previous research has mainly focused on recognizing handwritten Chinese c
Publikováno v:
Image and Vision Computing. 31:958-968
This paper proposes a method for keyword spotting in off-line Chinese handwritten documents using a contextual word model, which measures the similarity between the query word and every candidate word in the document by combining a character classifi
Publikováno v:
ICDAR
This paper describes the Chinese handwriting recognition competition held at the 12th International Conference on Document Analysis and Recognition (ICDAR 2013). This third competition in the series again used the CASIA-HWDB/OLHWDB databases as the t
Publikováno v:
Document Analysis Systems
This paper investigates the effects of unsupervised language model adaptation (LMA) in handwritten Chinese text recognition. For no prior information of recognition text is available, we use a two-pass recognition strategy. In the first pass, the gen
Publikováno v:
ICDAR
In the Chinese handwriting recognition competition organized with the ICDAR 2011, four tasks were evaluated: offline and online isolated character recognition, offline and online handwritten text recognition. To enable the training of recognition sys