- A Python application that uses camera input to train a Support Vector Machine (SVM) to respond to specific actions using neural networks to train a dataset.
- To complete an AI project that is a fully-functioning camera classifier by combining principles of computer vision with principles of neural networks.
Python Jupiter Notebook Tkinter OpenCV image
Camera will continuously run in the background.
A GUI window is used to capture images of objects to store at a specified location.
It will then Create different classes for different objects.
It will predict the object shown in the camera.
- Recent advances in deep learning made tasks such as Image and speech recognition possible.
The idea of Scanning and image recognition is very fascinating.
I wanted to dive deeper into the idea like google lens.
- I have built a deep neural network that can recognize images with an high accuracy of while explaining the techniques used throughout the process.
- Deep learning excels in recognizing objects in images as it’s implemented using 3 or more layers of artificial neural networks where each layer is responsible for extracting one or more feature of the image.
- I was able to build an artificial convolutional neural network that can recognize images with a high accuracy
- I did so by pre-processing the images to make the model more generic, split the dataset into a number of batches and finally build and train the model.
I wish to add Text to speech voice assistant for visually Impaired people.
- I would like to add Search results in accordance with the image for better exposure to the product.
