Quantifier Sign Language Recognition Image Dataset

Sign language recognition plays an important role in helping hearing-impaired individuals communicate with others. However, current sign language recognition technologies face challenges with the diversity of gestures and the accuracy of quantity expression. Many solutions perform poorly due to the lack of rich and high-quality data support. This dataset focuses on quantifier sign language recognition, aiming to improve the accuracy and efficiency of quantifier sign language recognition. The dataset is collected using high-definition camera equipment under controlled lighting, including sign language action samples from different age groups and genders. Quality control includes multiple rounds of annotation, consistency checks, and reviews by sign language experts. The annotation team consists of 10 sign language experts to ensure professionalism and accuracy in annotations. Data preprocessing uses techniques such as image normalization and background removal to ensure prominent gesture features. The final data is stored in JPG format and organized by gesture category. The data quality is high, with annotation accuracy exceeding 98%, maintaining consistency and integrity. In annotation methods, an innovative sign language motion trajectory calibration technique is introduced. The dataset solves the problem of quantifier recognition and can significantly improve the accuracy of recognition systems in quantifier recognition. Compared with other sign language datasets, it provides richer quantifier samples and higher annotation accuracy. Its scarcity and diversity give the dataset extensibility and universality in the field of sign language recognition, applicable to multiple sign language recognition tasks.