## 4 Proposed Communication System for D-M
### Communication System Block Details  
Figure 4 is a full duplex communication system between the D-M and the ND-M. The figure shows the implementation of the proposed system. The D-M person is provided with an interface where the hand gestures are acquired using an LMD connected to the PC-based system. The LMD captures hand gestures as images and forwards them to the PC, where it is further processed. The details of the processing are discussed later. The  
Figure 3: Novel features of the full duplex communication system. system supports multiple sign language datasets, i.e., ASL, PSL, and SSL. The system is reconfigurable, and more datasets can be added in the future. The acquired image data is processed through multiple stages and is then converted into a voice message. The ND-M person can listen to the voice message. The speech or voice data is generated through the PC sound card output. In response, the ND-M person records a voice message which is acquired by the PC using sound card input. The voice message is then processed by the software application and converted into text and hand gesture images. The D-M person is then able to read the text or see the images. Similarly, an ND-M person can also initiate a conversation. The proposed system is low-cost, user-friendly, and does not require any special training. These are the main features that make it easy and attractive for people to use this system. The sign language datasets selected for this system are based on the number of people using these sign language datasets. The selection of ASL, PSL, and SSL is based on the availability and size of datasets and the number of people using them. A small dataset means the size of the training dataset will also be small; hence the trained system will be less effective or accurate, while a large dataset results in higher accuracy but a slower response. Considering this, medium-sized datasets are selected. The system provides a training mode where the user can check the accuracy of the system while undergoing training. In this mode, the hand gestures of the D-M person are processed, and the detected results are displayed without going through the text to speech to text conversion. The D-M person can vary hand gestures to check for detection accuracy. In this mode, the system also updates the database by replacing existing images with higher-quality images. The ML-based process compares the newly acquired images with the stored images and then decides between replacing the existing image with the new one or storing the new image along with the existing image or not storing the new image at all. Having more image files means an improved dataset which will increase accuracy, but this also means the system will require more processing time. It is important to have a balance between these parameters. This decision is taken by the ML-based implementation. The ML algorithm also reviews the user-stored data, which helps to increase processing speed and accuracy. For example, if a user profile shows that the user only understands SSL, then the process will bypass the language detection step for this particular user. Similarly, the user can update the profile by adding more sign language datasets and other parameters. The system also maintains a performance record, both for the individual user and the overall system, using the stored user data and new data acquired through the training and normal modes. Figure 5 shows the ML-based implementation. The block diagram shows the input, output, and hidden layers displaying various activities. The data is fed through the input layer, as shown by '1'. The input data includes both the hand gesture image data and the user data, which is stored as part of the user profile. In '2', the user profile data is processed. The user can update the profile to reflect any changes. The next step, '3', is the training mode. This step is used when the training mode is selected. In normal mode, the ML algorithm can use some options from this step. The language detection is done in step '4'.