cnn keras svhn digit detection computer-vision tensorflow-backend sequence-prediction svhn-cnn

Classification and Detection with Convolutional Neural Networks

Four digit(horizontal) sequence prediction with CNN using Keras with TensorFlow backend

Abstract:

Automatic detection of digits and numbers is a task where recent work in neural networks and computer vision has shown a lot of promise. The goal of this project is to replicate earlier results [2][1] using multiple Convolutional Neural Network (CNN) models to predict a sequence of numbers. The Street View House Number (SVHN) data set which has ~250,000 labelled images were used in this study. About 150,000 samples from this dataset were used to train three different CNN models: designed architecture, VGG-16, Pre-Trained VGG-16 to predict a sequence of up to four digits.

Video Results (Click image to view demo on YouTube)

https://www.youtube.com/watch?v=ZDRt21VEOpA "SVHN Detection using CNN")

The reported train and test sequence accuracies are 91.53% Train and 85.4% Test for own architecture, 96.6% Train and 91.24% Test for Pre-Trained VGG-16, 90.8% and 82.1% train and test accuracies for the VGG-16 model. The bench mark is a reported 96% test accuracy on the entire data set [1]. The pre-trained VGG-16 model which uses the ‘ImageNet’ weights showed the best accuracy. This model is tested on real-life detection scenarios using a 10-level image pyramid with sliding window detection to tackle digit sequence variance with size and scale. Ian GoodFellow's Reference Paper https://arxiv.org/pdf/1312.6082.pdf