The objective of the project is to implement a simple image classification based on the k-Nearest Neighbour and a deep neural network.
SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data formatting but comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.
The images come in two formats as follows.
Format 1 : Original images with character level bounding boxes.
Format 2 : MNIST-like 32-by-32 images centered around a single character (many of the images do contain some distractors at the sides).
Acknowledgement for the datasets. Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Reading Digits in Natural Images with Unsupervised Feature Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011. PDF http://ufldl.stanford.edu/housenumbers as the URL for this site when necessary