The accurate detection and classification of occluded objects in images present an ongoing challenge in the field of computer vision. In our current work, we tackle a situation where we need to identify the objects that are handwritten digits from the MNIST dataset1. Our proposed approach is decomposed into two steps. In Step 1, a classification model based on Convolutional Neural Networks is designed and implemented to classify digits that are augmented with noise at varying degrees. In Step 2, we propose to introduce a model based on Single Shot Detector and tailor it to our purpose. Once a possible digit is located in an image, it will be then classified by our model from Step 1. While we are still finalizing Step 2 of our proposed approach, the experiment results from the classification model we propose for Step 1 are promising. In this paper, we are presenting the details of this model, including the architecture of the model, hyperparameters, dataset augmentation, etc. We believe that our proposed CNN-based model for classifying noisy digits is also applicable to classifying other objects, such as letters, characters, etc.