Abstract
This paper presents ClapperText, a benchmark dataset for handwritten and printed text recognition in visually degraded and low-resource settings. The dataset is derived from 127 World War II-era archival video segments containing clapperboards that record structured production metadata such as date, location, and camera-operator identity. ClapperText includes 9,813 annotated frames and 94,573 word-level text instances, 67% of which are handwritten and 1,566 are partially occluded. Each instance includes transcription, semantic category, text type, and occlusion status, with annotations available as rotated bounding boxes represented as 4-point polygons to support spatially precise OCR applications. Recognizing clapperboard text poses significant challenges, including motion blur, handwriting variation, exposure fluctuations, and cluttered backgrounds, mirroring broader challenges in historical document analysis where structured content appears in degraded, non-standard forms. We provide both full-frame annotations and cropped word images to support downstream tasks. Using a consistent per-video evaluation protocol, we benchmark six representative recognition and seven detection models under zero-shot and fine-tuned conditions. Despite the small training set (18 videos), fine-tuning leads to substantial performance gains, highlighting ClapperText's suitability for few-shot learning scenarios. The dataset offers a realistic and culturally grounded resource for advancing robust OCR and document understanding in low-resource archival contexts. The dataset and evaluation code are available at this https URL.
Abstract (translated)
这篇论文介绍了ClapperText，这是一个用于在视觉退化和低资源环境下识别手写和印刷文本的基准数据集。该数据集来源于包含 clapboard（场记板）的127段第二次世界大战时期的档案视频片段，这些 clapboard 记录了生产元数据，如日期、地点和摄像师身份等结构化信息。ClapperText 包含9,813张注释图像帧和94,573个单词级别的文本实例，其中67%为手写文本，并且有1,566个部分被遮挡的实例。每个实例包括转录、语义类别、文本类型以及遮挡状态的信息，注释以旋转边界框的形式提供（表示为四点多边形），以便支持空间精确的OCR应用。 识别 clapboard 上的文字面临着许多挑战，比如运动模糊、手写风格的变化、曝光度波动和杂乱背景等。这些问题反映了在历史文档分析中遇到的更广泛的问题，在这种情况下，结构化内容以退化的非标准形式出现。我们提供全帧注释及裁剪后的单词图像，支持下游任务需求。 通过一致的视频级评估协议，我们在零样本（zero-shot）和微调条件下对六个代表性识别模型和七个检测模型进行了基准测试。尽管训练集规模较小（18段视频），但微调能够带来显著性能提升，这突显了ClapperText在少量数据学习场景中的适用性。 该数据集为推进低资源档案环境下的稳健OCR技术和文档理解提供了现实且文化相关的资源。数据集和评估代码可在[此处](https://this https URL)获取。
URL
https://arxiv.org/abs/2510.15557
