Educational OMR Answer Sheet Dataset for Automated Assessment via Object Detection

Overview This dataset contains scanned and photographed paper-based multiple-choice answer sheets designed for Optical Mark Recognition (OMR) using object detection models. The dataset was developed to support research on automated scoring frameworks based on deep learning object detection architectures. It has been used in the associated publication describing an automated paper-based multiple choice scoring framework. Two versions of the dataset are provided: datasetv1 – Version used in the original research paper. datasetv2 – Enhanced version with upgraded architecture (YOLOv11) and modularized detection tasks. All sensitive personal information has been anonymized using a reproducible masking procedure prior to release. Dataset Structure datasetv1 (Paper Version) This version corresponds to the dataset used in the published paper. Single unified detection model YOLO format annotations Standard split into: train validation test dataset.yaml defines class names and dataset paths classes.txt lists object categories This version supports end-to-end detection of all required OMR components within a single model. datasetv2 (Enhanced Version) This is the upgraded and modularized dataset version. Key improvements: Migrated to YOLOv11 Detection tasks separated into independent models Increased flexibility and improved maintainability The dataset is divided into three independent detection tasks: 1. data_answer Detection of multiple-choice answer bubbles. classes_answer.txt dataset_answer.yaml train / val / test split 2. data_info Detection of student information fields (e.g., ID regions, structured metadata areas). classes_info.txt dataset_info.yaml train / val / test split 3. data_marker Detection of alignment markers and reference anchors used for geometric correction. classes_marker.txt dataset_marker.yaml train / val / test split This modular design enables: Independent training Improved detection accuracy More scalable system design Easier experimentation and benchmarking Data Split Each dataset version provides: Training set Validation set Test set The split ensures fair evaluation and reproducibility. Anonymization All answer sheets were anonymized before publication. Personal identifiers such as names, signatures, and sensitive information have been masked using a reproducible automated masking procedure. Intended Use This dataset is intended for: Research on Optical Mark Recognition (OMR) Object detection benchmarking Educational assessment automation Computer vision research in document analysis License This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Citation If you use this dataset, please cite: The associated research article This dataset DOI数据集概览 本数据集包含经扫描与拍摄的纸质多项选择题答题卡，旨在用于基于目标检测模型的光学标记识别（Optical Mark Recognition, OMR）任务。 本数据集旨在支撑基于深度学习目标检测架构的自动阅卷框架相关研究，已被用于描述该纸质多项选择题自动阅卷框架的关联学术出版物中。 本数据集提供两个版本： datasetv1 —— 原版研究论文使用版本 该版本对应已发表论文中采用的数据集。 采用单一统一检测模型，标注格式为YOLO格式，标准拆分方式分为： - 训练集（train） - 验证集（validation） - 测试集（test） 其中dataset.yaml用于定义类别名称与数据集路径，classes.txt列出目标类别。 该版本支持通过单模型端到端检测所有所需OMR组件。 datasetv2 —— 增强版本 本数据集为升级后的模块化版本，核心改进如下： 1. 迁移至YOLOv11架构 2. 将检测任务拆分为独立模型 3. 提升了灵活性与可维护性 本数据集分为三个独立检测任务： 1. data_answer：多项选择题答题框检测 配套文件：classes_answer.txt、dataset_answer.yaml，以及train/val/test拆分集 2. data_info：学生信息区域（如学号区域、结构化元数据区域）检测 配套文件：classes_info.txt、dataset_info.yaml，以及train/val/test拆分集 3. data_marker：用于几何校正的对齐标记与参考锚点检测 配套文件：classes_marker.txt、dataset_marker.yaml，以及train/val/test拆分集 该模块化设计可实现： - 独立训练 - 提升检测精度 - 更具可扩展性的系统设计 - 更便捷的实验与基准测试 数据集拆分 每个数据集版本均提供训练集、验证集与测试集，该拆分方式保障了评估的公平性与实验可复现性。 匿名化处理 本数据集发布前已通过可复现的自动化掩码流程对所有答题卡进行匿名化处理，姓名、签名等个人敏感标识符均已被遮盖。 适用场景 本数据集适用于： - 光学标记识别（OMR）相关研究 - 目标检测基准测试 - 教育测评自动化研究 - 文档分析领域的计算机视觉研究 授权协议 本数据集采用知识共享署名4.0国际许可（Creative Commons Attribution 4.0 International, CC BY 4.0）协议发布。 引用说明 若您使用本数据集，请引用： 1. 关联学术研究论文 2. 本数据集的DOI标识