One of the major reasons for the lack of decipherment of the Indus Valley Script is that even its set of signs have not been precisely identified. This paper proposes a fully automated method leveraging computer vision and statistical modeling to analyze the Indus Valley Script. The automated method includes sign segmentation using adaptive thresholding and morphology, followed by visual feature extraction with VGG16 deep learning and dimensionality reduction via principal component analysis. Clustering with K-means grouped the previously proposed 417 Indus Valley Script signs into 50 clusters that would be a more reasonable number if the Indus Valley Script is a syllabic or alphabetic script. The automated method also builds a first-order Markov chain on the 50 sign clusters. The Markov model reveals some frequent self-loops and other interesting patterns that hint at the grammar of the underlying language of the Indus Valley Script. The grammar could lead to the identification of related languages, aiding the decipherment of the Indus Valley Script. The proposed automated method could be adapted to the study of other undeciphered scripts.