Date of Award


Document Type


Degree Name

Master of Science (MS)


Electrical Engineering and Computer Science

First Advisor

Debasis Mitra

Second Advisor

Xianqi Li

Third Advisor

Eraldo Ribeiro

Fourth Advisor

Brian Lail


This research introduces ASR-net(Ancient Script Recognition), a groundbreaking system that automatically digitizes ancient Indus seals by converting them into coded text, similar to Optical Character Recognition for modern languages. ASR-net, with an 95% success rate in identifying individual symbols, aims to address the crucial need for automated techniques in deciphering the enigmatic Indus script. Initially Yolov3 is utilized to create the bounding boxes around each graphemes present in the Indus Valley Seal. In addition to that we created M-net(Mahadevan) model to encode the graphemes. Beyond digitization, the paper proposes a new research challenge called the Motif Identification Problem (MIP) related to recurring patterns (motifs) on Indus seals that appear to have specific functions within certain periods of the civilization. Despite challenges in applying deep learning to MIP, The database was created to store the ImageID, Image, the list of encoded graphemes present in that particular image followed by the Motif on the IVC Seal in the structured format.