Related Experiment Video
Updated: Aug 4, 2025
06:37
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
Published on: December 15, 2023
3.9K
DAN: A Segmentation-Free Document Attention Network for Handwritten Document Recognition.
Summary
This study introduces the Document Attention Network, an end-to-end system for handwritten text recognition that bypasses traditional segmentation steps. This novel approach achieves competitive character error rates on benchmark datasets, simplifying document analysis.
Area of Science:
- Computer Vision
- Machine Learning
- Document Analysis
Background:
- Unconstrained handwritten text recognition is complex, typically requiring separate line segmentation and recognition steps.
- Existing methods rely heavily on accurate segmentation, which can be a bottleneck for performance.
Purpose of the Study:
- To propose a novel end-to-end, segmentation-free architecture for handwritten document recognition.
- To develop a system capable of recognizing text and logical layout tokens simultaneously.
Main Methods:
- The Document Attention Network (DAN) utilizes a Fully Convolutional Network (FCN) encoder for feature extraction.
- A stack of transformer decoder layers enables recurrent token-by-token prediction of characters and layout tags.
- The model is trained without segmentation labels, taking entire documents as input.
Main Results:
- Achieved competitive character error rates (CER) on the READ 2016 dataset at page (3.43%) and double-page (3.70%) levels.
- Attained a CER of 4.54% on the RIMES 2009 dataset at the page level.
- Demonstrated the effectiveness of a segmentation-free approach for handwritten text recognition.
Conclusions:
- The Document Attention Network offers a viable and competitive alternative to traditional segmentation-based methods.
- This end-to-end approach simplifies the handwritten text recognition pipeline.
- The released source code and pre-trained models facilitate further research and application.
