The use of advanced deep learning algorithms, especially convolutional neural networks and gated recurrent units, has been a popular approach to improve the performance of handwriting recognition algorithms. Our results show that this integration significantly reduces loss and improves the HTR process, demonstrating the effectiveness of deep learning models using HTR. Furthermore, our findings show that using GRU increases CNN performance, providing further evidence for the importance of considering network task schedule and learning algorithm options when designing HTR algorithms. Overall, this research provides valuable insights into the ability of deep learning models to improve HTR performance, as well as HTR frameworks that highlight the importance of careful consideration of the underlying processes.