手持视频文档扫描：一种用于多页文档扫描的鲁棒端侧模型
Handheld Video Document Scanning: A Robust On-Device Model for Multi-Page Document Scanning
AI总结：
针对智能手机多页文档扫描耗时繁琐的问题，提出支持手持操作的视频流自动多页扫描方法，含高效端侧模型、新型数据采集标注技术，在PUCIT数据集上达SOTA。
摘要：
智能手机上的文档采集应用已成为广受欢迎的文档数字化工具。对许多人而言，用智能手机采集文档比使用专用复印机或扫描仪更便捷，即便数字化质量稍低。但当用户需要对多页文档进行数字化时，使用智能手机完成数字化会变得极为耗时且繁琐。\n在本研究中，我们提出了一种新方法，可在用户翻阅文档页面时，从视频流中自动扫描多页文档。与以往需要将手机固定在三脚架上等受限设置的方法不同，我们的技术设计支持用户手持手机操作。该技术经过训练，可对手持扫描固有的运动和不稳定性保持鲁棒性。本研究的主要贡献包括：（1）一种高效的端侧深度学习模型，对手持扫描场景具备准确性与鲁棒性；（2）一种面向视频文档扫描的新型数据采集与标注技术；（3）在PUCIT翻页数据集上取得了当前最优结果。
英文摘要：
Document capture applications on smartphones have emerged as popular tools for digitizing documents. For many individuals, capturing documents with their smartphones is more convenient than using dedicated photocopiers or scanners, even if the quality of digitization is lower. However, using a smartphone for digitization can become excessively time-consuming and tedious when a user needs to digitize a document with multiple pages. In this work, we propose a novel approach to automatically scan multi-page documents from a video stream as the user turns through the pages of the document. Unlike previous methods that required constrained settings such as mounting the phone on a tripod, our technique is designed to allow the user to hold the phone in their hand. Our technique is trained to be robust to the motion and instability inherent in handheld scanning. Our primary contributions in this work include: (1) an efficient, on-device deep learning model that is accurate and robust for handheld scanning, (2) a novel data collection and annotation technique for video document scanning, and (3) state-of-the-art results on the PUCIT page turn dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
