BSL-Static-48: A Signer-Independent Dataset of Anonymized Images and MediaPipe Hand Landmarks for Bangla Sign Language Recognition

BSL-Static-48 is a specialized Bangla Sign Language (BSL) dataset designed for Signer-Independent recognition. It encompasses 48 distinct classes, categorized into 10 digits (D0–D9) and 38 alphabets (L1–L38). This version (Version 2) implements a rigorous logic to prevent data leakage and ensure model generalizability. Key Features & Methodology: Data Source & Anonymization: Data was collected from 5 distinct volunteers (V01 to V05), totaling 14,568 original images. To ensure privacy, all images underwent facial blurring using an OpenCV-based DNN face detector, which did not compromise the accuracy of hand landmark detection. Data Augmentation & Integrity: To double the dataset size and ensure hand-dominance invariance, horizontal mirroring was applied. To prevent Data Leakage, original images and their mirrored counterparts are strictly maintained within the same data partitions. 126-Dimensional Feature Extraction: Pre-processed features are provided in NumPy (.npy) format. Each sample is a 126-dimensional vector (21 landmarks per hand × 3 coordinates (x, y, z) × 2 hands). Landmark extraction using MediaPipe Holistic achieved a success rate of 99.97%. Signer-Independent Protocol: This dataset provides a dedicated volunteer-based split: Train Set (20,438 samples): Data from Volunteers V01, V02, V03, and V04. Validation Set (2,956 samples): Data from Volunteers V01, V02, V03, and V04. Independent Test Set (5,742 samples): Data strictly from Volunteer V05. Transparency: Includes metadata CSVs mapping every feature file to its original volunteer and assigned split, along with quantitative verification reports.BSL-Static-48是一款专为手语者无关（Signer-Independent）识别任务设计的专业化孟加拉语手语（Bangla Sign Language, BSL）数据集。该数据集涵盖48个独立类别，分为10个数字类（D0–D9）与38个字母类（L1–L38）。本次发布的V2版本采用了严格的逻辑机制以规避数据泄露，并保障模型的泛化能力。 核心特性与研究方法： 数据来源与匿名化处理：数据采集自5名不同的志愿者（V01至V05），原始图像共计14568张。为保障隐私安全，所有图像均通过基于OpenCV的深度神经网络（Deep Neural Network, DNN）人脸检测器进行了面部模糊处理，且该处理未对手部关键点检测的精度造成负面影响。 数据增强与完整性保障：为将数据集规模翻倍并确保手势主导手不变性，研究团队采用了水平镜像变换手段。为防止数据泄露，原始图像及其镜像版本会被严格划分至同一数据分区中。 126维特征提取：研究团队提供了以NumPy（.npy）格式存储的预处理特征。每个样本为126维向量（单只手部21个关键点 × 3个坐标(x,y,z) × 2只手部）。采用MediaPipe Holistic进行关键点提取，其提取成功率可达99.97%。 手语者无关划分协议：本数据集采用基于志愿者的专属划分方案： 训练集（20438个样本）：数据来源于志愿者V01、V02、V03与V04。 验证集（2956个样本）：数据来源于志愿者V01、V02、V03与V04。 独立测试集（5742个样本）：数据仅来自志愿者V05。 透明度保障：数据集附带元数据CSV（Comma-Separated Values, CSV）文件，可将每个特征文件与其对应的原始志愿者及分配的数据分区进行映射，同时还包含量化验证报告。