MaiSci ☕

  Unlike single digit detection problem like MNIST, images in SVHN dataset have more than one digit in series. SVHN Samples. Ref: http://ufldl.stanford.edu/housenumbers/ In this project, we used MSER detection and modified pre-trained VGG16 convolutional neural network to predict the digit sequence given a random image in SVHN dataset. Besides VGG16, we also used non-pre-trained VGG16 and self-built a much smaller CNN as comparisons. VGG16 Architecture. Ref: https://neurohive.io/en/popular-networks/vgg16/  Self-built CNN  The pipeline of the prediction is as: MSER detect blob regions →  CNN  find ROIs(regions of interest) → Combine overlapped/near ROIs →  CNN  Find Digits For training purposes, we mark each image with five integers as the label. The first four integers represent digits in the image, with 0 as no digit for that position and 1-10 representing 1-9 and 0. The fifth integer represents whether the area has any digit, with 0 represent a non-digit area and 1 as a window with d