mlsanityData to 14 Oct 2026

Show, don't tell -- Providing Visual Error Feedback for Handwritten Documents

arXiv 2601.09586 ↗First posted 14 Jan 2026cs.CV cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 14 Jan 2026.

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6 of 30 references.

References · 6

  1. 2015An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioncited by 520
  2. 2021TrOCR: Transformer-based Optical Character Recognition with Pre-trained Modelscited by 279
  3. 2019Character Region Awareness for Text Detectioncited by 278
  4. 2021PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Networkcited by 49
  5. 2021TextStyleBrush: Transfer of Text Aesthetics from a Single Examplecited by 49
  6. 2022Towards End-to-end Handwritten Document Recognitioncited by 1

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