Hand gesture recognition is a pivotal area in artificial intelligence, enabling seamless human–machine interaction across diverse applications such as home appliances, electronic devices, and public transportation systems. This paper introduces the Moghbeli Persian Sign Language (MPSL) database, a comprehensive image-based resource designed to advance Persian sign language recognition research. The database comprises 107,630 images organized into 47 classes, including 37 Persian alphabet characters and 10 numerical digits (0–9). The images were extracted from 1–2-minute videos captured using a mobile phone camera and involve 18 participants (seven women and eleven men) fluent in Persian sign language. Data acquisition was performed under varying lighting conditions and imaging environments to enhance dataset diversity and robustness. Each class contains 2,290 images, resulting in a balanced large-scale database suitable for training and evaluation purposes. To validate the proposed database, four deep learning and hybrid recognition models were evaluated. The experimental results demonstrate that all evaluated methods achieve high recognition performance on the Moghbeli Persian Sign Language database. Among the evaluated methods, the hybrid model combining Oriented FAST and Rotated BRIEF, Gabor filters, and a Convolutional Neural Network achieved the highest average accuracy of 99.94% while requiring only 241,343 trainable parameters. The obtained results confirm that the proposed database provides sufficiently discriminative visual information for reliable recognition of Persian sign language characters and numerical digits. This database therefore represents a valuable benchmark resource for future research in Persian sign language recognition.