## 2 Materials and Methods
### Deployment of the TacPic System  
The TacPic system leverages the technologies of AI cloud computing to create TEM for VIB education. AI is deployed to provide a lightweight implementation that teachers and parents of the visually impaired students can easily use on their devices without needing expensive hardware, training on using artificial intelligence and 3D reconstruction, 3D modelling, or 3D printing. This is deployed on a web interface with an Amazon Web Service (AWS) backend. By leveraging the scalability of the deployment on the AWS servers, we were able to allocate the appropriate amount of processing power to each step of the development pipeline.  
The system infrastructure is shown in Figure 3. The user first inputs an image through the web interface and sets the different parameters, such as the type of tactile material, the material size, and rotations. Then, when the user presses the \"Submit\" button, the web interface sends the image and its parameters to the AWS backend and starts the process. The succeeding web interface fetches the STL file from the output S3 bucket and displays the resulting TEM for inspection by the user. The TEM creation process is as follows:  
1. The user uploads an image to the customization interface and sets the parameters for the image type, size, etc.
2. The image is stored in a user-specific S3 bucket division with its specific parameters.
3. This event triggers a Lambda function that passes the image to two Lambda functions of masking and pre-processing. After both processes are completed, they each store their resulting outputs in another S3 bucket.
4. Next, another Lambda function extracts the needed information label from the previous step.
5. Then, the image from the previous step is processed by the TacPic Modeling Lambda function to create the 3D models of the TEM before combining the braille words and images. Finally, the 3D STL file from this step is stored in the output S3 bucket.
6. The 3D viewer interface collects the 3D STL file from the S3 bucket and displays an interactive 3D model that can be inspected.  
The development of the system was undertaken in a Python environment. The algorithms used were based on the Python implementation within multiple libraries, namely, TensorFlow, OpenCV, and NumPy. The TensorFlow library was used to perform the object detection and instance segmentation on images. The OpenCV library was used to perform the image processing algorithms. Finally, the NumPy library was used for image registration and 3D STL generation. This implementation was applied in the AWS platform for ease of integration with different features. It is beneficial if the algorithm is deployed within a web-based application or a mobile application. Subsequently, the AWS Elastic Container Registry (ECR) was used to store the different algorithms. The algorithms were stored using a Docker container and connected to the AWS Lambda service to enable the  
Figure 3: The processes implemented though cloud computing using the AWS functions.  
container to be run remotely without a dedicated server. Different AWS S3 buckets were used as the storage for the images and STL files used during the testing. The whole process within the AWS platform is shown in Figure 3.