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

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 September 2025 V1 Latest version Share on HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Authors : Mohammed Hamdan 0000-0002-2711-2403 [email protected] , Abderrahmane Rahiche , and Mohamed Cheriet Authors Info & Affiliations https://doi.org/10.22541/au.175924882.28819604/v1 120 views 113 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share 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 memoryaugmented 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 postprocessing 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 stateof-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. Supplementary Material File (hand_research_paper_to_resubmit.pdf) Download 2.60 MB Information & Authors Information Version history V1 Version 1 30 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords dual-path feature extraction handwritten document recognition hierarchical attention layout analysis novel architectures post-processing mt5 model reproducibility signal processing and analysis transformer decoder Authors Affiliations Mohammed Hamdan 0000-0002-2711-2403 [email protected] View all articles by this author Abderrahmane Rahiche View all articles by this author Mohamed Cheriet View all articles by this author Metrics & Citations Metrics Article Usage 120 views 113 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mohammed Hamdan, Abderrahmane Rahiche, Mohamed Cheriet. HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis. Authorea . 30 September 2025. DOI: https://doi.org/10.22541/au.175924882.28819604/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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