4 code implementations • 29 Jul 2022 • Dezhi Peng, Lianwen Jin, Yuliang Liu, Canjie Luo, Songxuan Lai
Utilizing the proposed weakly supervised learning framework, PageNet requires only transcripts to be annotated for real data; however, it can still output detection and recognition results at both the character and line levels, avoiding the labor and cost of labeling bounding boxes of characters and text lines.
1 code implementation • 15 Dec 2021 • Dezhi Peng, Xinyu Wang, Yuliang Liu, Jiaxin Zhang, Mingxin Huang, Songxuan Lai, Shenggao Zhu, Jing Li, Dahua Lin, Chunhua Shen, Xiang Bai, Lianwen Jin
For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotation of a single-point for each instance.
Ranked #3 on Text Spotting on SCUT-CTW1500
1 code implementation • 13 Aug 2021 • Ruben Tolosana, Ruben Vera-Rodriguez, Carlos Gonzalez-Garcia, Julian Fierrez, Aythami Morales, Javier Ortega-Garcia, Juan Carlos Ruiz-Garcia, Sergio Romero-Tapiador, Santiago Rengifo, Miguel Caruana, Jiajia Jiang, Songxuan Lai, Lianwen Jin, Yecheng Zhu, Javier Galbally, Moises Diaz, Miguel Angel Ferrer, Marta Gomez-Barrero, Ilya Hodashinsky, Konstantin Sarin, Artem Slezkin, Marina Bardamova, Mikhail Svetlakov, Mohammad Saleem, Cintia Lia Szucs, Bence Kovari, Falk Pulsmeyer, Mohamad Wehbi, Dario Zanca, Sumaiya Ahmad, Sarthak Mishra, Suraiya Jabin
This article presents SVC-onGoing, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB and SVC2021_EvalDB, and standard experimental protocols.
1 code implementation • 1 Jun 2021 • Ruben Tolosana, Ruben Vera-Rodriguez, Carlos Gonzalez-Garcia, Julian Fierrez, Santiago Rengifo, Aythami Morales, Javier Ortega-Garcia, Juan Carlos Ruiz-Garcia, Sergio Romero-Tapiador, Jiajia Jiang, Songxuan Lai, Lianwen Jin, Yecheng Zhu, Javier Galbally, Moises Diaz, Miguel Angel Ferrer, Marta Gomez-Barrero, Ilya Hodashinsky, Konstantin Sarin, Artem Slezkin, Marina Bardamova, Mikhail Svetlakov, Mohammad Saleem, Cintia Lia Szücs, Bence Kovari, Falk Pulsmeyer, Mohamad Wehbi, Dario Zanca, Sumaiya Ahmad, Sarthak Mishra, Suraiya Jabin
This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021).
no code implementations • 20 Jul 2020 • Zhe Li, Lianwen Jin, Songxuan Lai, Yecheng Zhu
Handwritten mathematical expression recognition (HMER) is an important research direction in handwriting recognition.
no code implementations • 13 Nov 2019 • Songxuan Lai, Lianwen Jin, Luojun Lin, Yecheng Zhu, Huiyun Mao
To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures.
no code implementations • 3 May 2019 • Songxuan Lai, Lianwen Jin
In this paper, we propose a novel set of features for offline writer identification based on the path signature approach, which provides a principled way to express information contained in a path.
3 code implementations • 3 Dec 2018 • Shuaitao Zhang, Yuliang Liu, Lianwen Jin, Yaoxiong Huang, Songxuan Lai
The feature of the former is first enhanced by a novel lateral connection structure and then refined by four carefully designed losses: multiscale regression loss and content loss, which capture the global discrepancy of different level features; texture loss and total variation loss, which primarily target filling the text region and preserving the reality of the background.
no code implementations • 19 May 2017 • Songxuan Lai, Lianwen Jin, Weixin Yang
Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification.
no code implementations • 24 Feb 2017 • Songxuan Lai, Lianwen Jin, Weixin Yang
This paper presents an investigation of several techniques that increase the accuracy of online handwritten Chinese character recognition (HCCR).