Show, don’t tell – Providing Visual Error Feedback for Handwritten Documents

ACL ARR 2025 May Submission3645 Authors

Abstract:

Handwriting remains an essential skill, particularly in education.
Therefore, providing visual feedback on handwritten documents is an important but understudied area.
We outline the many challenges when going from an image of handwritten input to correctly placed informative error feedback.
We empirically compare modular and end-to-end systems and find that both approaches currently do not achieve acceptable overall quality.
We identify the major challenges and outline an agenda for future research.

Paper Type: Long

Research Area: Human-Centered NLP

Research Area Keywords: human-AI interaction/cooperation, user-centered design

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 3645