HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis

2024-12-25

AI Summary

To address the performance limitations of conventional pipeline-based approaches for handwritten document recognition and layout analysis—caused by high variability in handwriting styles and complex page layouts—this paper proposes an end-to-end, segmentation-free, multi-scale unified modeling framework. Our method introduces a hierarchical memory-augmented sparse attention decoder and a Multi-Scale Adaptive Processing (MSAP) architecture, integrating gated depthwise separable convolutions and octave convolutions in the encoder. We further incorporate curriculum learning and a domain-adaptive mT5-based post-processing module. On the READ 2016 benchmark, our approach achieves state-of-the-art performance on both tasks simultaneously: line-level and page-level character error rates (CER) are reduced by 59.8% and 31.2%, respectively, with only 5.60 million parameters. To our knowledge, this is the first method to jointly advance both handwritten text recognition and layout analysis under a single, lightweight, end-to-end paradigm.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Handwritten document recognition (HDR) is one of the most challenging tasks in the field of computer vision, due to the various writing styles and complex layouts inherent in handwritten texts. Traditionally, this problem has been approached as two separate tasks, handwritten text recognition and layout analysis, and struggled to integrate the two processes effectively. This paper introduces HAND (Hierarchical Attention Network for Multi-Scale Document), a novel end-to-end and segmentation-free architecture for simultaneous text recognition and layout analysis tasks. Our model's key components include an advanced convolutional encoder integrating Gated Depth-wise Separable and Octave Convolutions for robust feature extraction, a Multi-Scale Adaptive Processing (MSAP) framework that dynamically adjusts to document complexity and a hierarchical attention decoder with memory-augmented and sparse attention mechanisms. These components enable our model to scale effectively from single-line to triple-column pages while maintaining computational efficiency. Additionally, HAND adopts curriculum learning across five complexity levels. To improve the recognition accuracy of complex ancient manuscripts, we fine-tune and integrate a Domain-Adaptive Pre-trained mT5 model for post-processing refinement. Extensive evaluations on the READ 2016 dataset demonstrate the superior performance of HAND, achieving up to 59.8% reduction in CER for line-level recognition and 31.2% for page-level recognition compared to state-of-the-art methods. The model also maintains a compact size of 5.60M parameters while establishing new benchmarks in both text recognition and layout analysis. Source code and pre-trained models are available at : https://github.com/MHHamdan/HAND.

Problem

• Handwritten Document Recognition
• Layout Analysis
• Integrated Approach

Innovation

• HAND
• joint recognition and analysis
• ancient manuscript identification