EDBT 2026 Demo / reviewers in the wild / expert
Tsui Hin Lin
dblp:299/7650
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 67% Image recognition and object detection · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › document analysis › document information extraction
key information extraction |
0.5 | 1 | 2021 | MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding · ACM Multimedia 2021 |
Natural language and speech › Information extraction and text analysis
named entity recognition |
0.5 | 1 | 2021 | MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding · ACM Multimedia 2021 |
Computer vision › Image recognition and object detection › scene text spotting
text detection and recognition |
0.5 | 1 | 2021 | MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding · ACM Multimedia 2021 |
Methods — techniques the papers use, named apart from their topics
deep learning · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and UnderstandingabstractWe present MMOCR---an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction. MMOCR implements 14 state-of-the-art algorithms, which is significantly more than all the existing open-source OCR projects we are aware of to date. To facilitate future research and industrial applications of text recognition-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of text detection, recognition and understanding. MMOCR is publicly released at https://github.com/open-mmlab/mmocr. Zhanghui Kuang, Zhizhong Li 0002, Xiaoyu Yue, Tsui Hin Lin, Jianyong Chen, Huaqiang Wei, Yiqin Zhu, Kai Chen 0026, Wayne Zhang 0001, Dahua Lin |
ACM Multimedia | 5 |