Continuous gesture recognition is a crucial task in human-computer interaction. Unlike isolated gesture recognition, where individual gestures are analyzed independently, continuous recognition involves detecting and classifying multiple gestures seamlessly from continuous video streams. In this paper, we propose a method for continuous gesture recognition. Our proposed model operates in two stages: isolated gesture recognition and a sliding window-based approach for continuous gesture recognition. For isolated gesture recognition, we propose a dual encoder method named TDDNet, stand for Text-Enhanced DDNet, that integrates a skeleton encoder based on the DDNet model [6] with a text encoder based on CLIP. We evaluate our model on a self-collected dataset comprising 19 gestures relevant to human-COBOT interaction, collected from 50 subjects. Experimental results demonstrate that our model improves isolated gesture recognition accuracy from 84.2% to 85.5%, while for continuous gesture recognition, the model achieves a performance of 66.60%, compared to 66.00% of the baseline model. The source code is publicly available at https://github.com/duclvQ/improved_DDNet.