Shahrood University of Technology Journal of AI and Data Mining 2322-5211 14 2 2026 04 01 Sign Language Recognition Using a Hybrid Model Based on Convolutional Neural Networks and Hidden Markov Models 209 220 3763 10.22044/jadm.2025.16424.2766 EN Malihe Danesh Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran. Zahra Ahmadi Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran. Journal Article 2025 07 02 In recent years, sign language recognition has emerged as a major challenge in the fields of image processing and machine learning. People with hearing impairments use sign language to communicate, but the lack of automated tools to translate it has created significant communication barriers. This study presents a hybrid model based on convolutional neural networks (CNNs), transformers, and hidden Markov models (HMMs) to accurately recognize sign language gestures using the MNIST sign language dataset. The model first extracts image features from handwritten images using CNNs and then feeds these features into the Transformer model to process complex and long-term dependencies in the feature sequence. In the next step, to smooth the predictions and improve accuracy, a hidden Markov model is employed, which adjusts the final predictions based on previous sequences. The results show that the proposed model utilizing HMM achieves an accuracy of 99% and a sign error rate of 0.0098, demonstrating its high efficiency in recognizing hand gestures. This research represents an important step toward developing assistive devices for the deaf and enhancing human interaction. https://jad.shahroodut.ac.ir/article_3763_e4561c4e0c3ced8a2069306f356ded59.pdf