Four digit(horizontal) sequence prediction with CNN using Keras with TensorFlow backend
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.
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
The original stanford dataset has been reformatted from the '.mat' to '.h5' format for easier access.
All the .mat and .hdf5 files referenced in the code as well the reformatted data used (in helper.prepDataForCNN) can be downloaded from
https://u.pcloud.link/publink/show?code=kZsxCO7ZdnzmYqXWO6ydqkC5kz114f5zUUaV#returl=https%3A//u.pcloud.link/publink/show%3Fcode%3DkZsxCO7ZdnzmYqXWO6ydqkC5kz114f5zUUaV
http://ufldl.stanford.edu/housenumbers/ (Original .mat)
https://my.pcloud.com/publink/show?code=kZsPnA7ZuMoiRGBAw0X4k7lGrU2CyFH2liey (Pretrained VGG and designed)
VGGPreTrain 0.17 0.76 0.88 96.62 87.91 91.24
VGG16 0.49 1.10 0.9 90.8 86.87 82.16
Designed 0.41 0.87 0.75 91.53 87.99 85.40