{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/efficient-few-shot-learning-for-pixel-precise","title":"Efficient few-shot learning for pixel-precise handwritten document layout analysis","arxiv_id":"2210.15570","date":"2022-10-27","proceeding":null,"authors":["Axel De Nardin","Silvia Zottin","Matteo Paier","Gian Luca Foresti","Emanuela Colombi","Claudio Piciarelli"],"abstract":"Layout analysis is a task of uttermost importance in ancient handwritten document analysis and represents a fundamental step toward the simplification of subsequent tasks such as optical character recognition and automatic transcription. However, many of the approaches adopted to solve this problem rely on a fully supervised learning paradigm. While these systems achieve very good performance on this task, the drawback is that pixel-precise text labeling of the entire training set is a very time-consuming process, which makes this type of information rarely available in a real-world scenario. In the present paper, we address this problem by proposing an efficient few-shot learning framework that achieves performances comparable to current state-of-the-art fully supervised methods on the publicly available DIVA-HisDB dataset.","url_abs":"https://arxiv.org/abs/2210.15570v1","url_pdf":"https://arxiv.org/pdf/2210.15570v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"document-layout-analysis","task_name":"Document Layout Analysis"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-diva-hisdb","task":"Semantic Segmentation","dataset":"DIVA-HisDB","model":"WACV '23 (few-shot)","rank_in_archive_order":3,"of":3,"metrics":{"Mean IoU (class)":"96.30"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}