In recent years, sign language plays a vital role as a means of communication for individuals who are deaf or hard of hearing. However, the challenge lies in the fact that not everyone is proficient in sign language, leading to a communication gap between the deaf community and those who use spoken language. To address this issue, we have developed a system for converting American Sign Language (ASL) into English text in real-time, offering a solution that can bridge this gap. Our system leverages deep learning techniques that combine convolutional and recurrent neural networks to recognize hand movements and convert them into text. This innovative method has the potential to significantly improve accessibility and communication for the deaf community while maintaining a high degree of accuracy. Additionally, we conduct hand movement modeling by dissecting hand movement components, utilizing sign language-independent data. This modeling process is accomplished through the utilization of technologies such as TDNN, CNN, and DNN. It is important to note that while there have been advancements in sign language recognition technology, it is still in its nascent stages of development. We evaluate the performance of our proposed methods through a comprehensive analysis based on our internally generated standard dataset. This assessment helps us gauge the effectiveness and accuracy of our system in real-world applications.