Term of Award

Spring 2020

Degree Name

Master of Science in Mathematics (M.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Mathematical Sciences

Committee Chair

Ionut Emil Iacob

Committee Member 1

Goran Lesaja

Committee Member 2

Felix Hamza-Lup


The automatic character recognition task has been of practical interest for a long time. Nowadays, there are well-established technologies and software to perform character recognition accurately from scanned documents. Although handwritten character recognition from the manuscript image is challenging, the advancement of modern machine learning techniques makes it astonishingly manageable. The problem of accurately recognizing handwritten character remains of high practical interest since a large number of manuscripts are currently not digitized, and hence inaccessible to the public. We create our repository of the datasets by cropping each letter image manually from the manuscript images. The availability of datasets is the major obstacle in our experiment. However, we tackle the problem by using resampling and convolutional techniques for performing character recognition from manuscript images in our neural network model with this reduced training dataset. The experimental result shows that our proposed model outperforms the previous work.

OCLC Number


Research Data and Supplementary Material