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William Dinauer

dblp:405/3483 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › handwriting recognition
handwritten text recognition
0.912025
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
0.912025
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
YearPublicationVenuePosition
2025 Recontextualizing Revitalization: A Mixed Media Approach to Reviving the Nüshu Language
abstract
Nüshu 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
EMNLP6