Demo Data from EDBT 2026 / reviewers in the wild / expert
William Dinauer
dblp:405/3483
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Image recognition and object detection · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Image recognition and object detection › handwriting recognition
handwritten text recognition |
0.9 | 1 | 2025 | Recontextualizing Revitalization: A Mixed Media Approach to Reviving the Nüshu Language · EMNLP 2025 |
Computer vision › Image recognition and object detection › text recognition
optical character recognition |
0.9 | 1 | 2025 | Recontextualizing Revitalization: A Mixed Media Approach to Reviving the Nüshu Language · EMNLP 2025 |
Methods — techniques the papers use, named apart from their topics
fine-tuning · 1.7TrOCR · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Recontextualizing Revitalization: A Mixed Media Approach to Reviving the Nüshu LanguageabstractNüshu is an endangered language from Jiangyong County, China, and the world's only known writing system created and used exclusively by women.Recent Natural Language Processing (NLP) work has digitized small Nüshu-Chinese corpora, but the script remains computationally inaccessible due to its handwritten, mixedmedia form and dearth of multimodal resources.We address this gap with two novel datasets: NüshuVision, an image corpus of 500 rendered sentences in traditional vertical, right-to-left orthography, and NüshuStrokes, the first sequential handwriting recordings of all 397 Unicode Nüshu characters by an expert calligrapher.Evaluating five state-of-the-art Chinese Optical Character Recognition (OCR) systems on NüshuVision shows that all fail entirely, each yielding a Character Error Rate (CER) of 1.0.Fine-tuning Microsoft's TrOCR on NüshuVision lowers CER to 0.67, a modest yet meaningful improvement.These contributions establish the first multimodal foundation for Nüshu revitalization and offer a culturally grounded framework for language preservation. Ivory Yang, Hefan Zhang 0001, Yaning Jia, William Dinauer, Soroush Vosoughi |
EMNLP | 6 |