Analysis of Teeline Shorthand Recognition using Machine Learning and Deep Learning Techniques
Shivaprakash1, Vishwanath C Burkpalli2, B. S. Anami3

1Mr. Shivaprakash*, Department of Computer Science and Engineering Government Engineering College, Devagiri, Haveri, India
2Dr. Vishwanath C Burkpalli, Department of Information Science and Engineering P.D.A. College of Engineering Gulbarga, India.
3Dr. B. S. Anami, Principal, K.L.E. Institute of Technology Hubli, India
Manuscript received on January 12, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 2133-2138 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1569029420/2020©BEIESP | DOI: 10.35940/ijitee.D1569.029420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: In order to take notes of the speech delivered by the VIPs in the short time short hand language is employed. Mainly there are two shorthand languages namely Pitman and Teeline. An automatic shorthand language recognition system is essential in order to make use of the handheld devices for speedy conversion to the original text. The paper addresses and compares the recognition of the Teeline alphabets using the Machine learning (SVM and KNN) and deep learning (CNN) techniques. The dataset has been prepared using the digital pen and the same is processed and stored using the android application. The prepared dataset is fed to the proposed system and accuracy of recognition is compared. Deep learning technique gave higher accuracy compared to machine learning techniques. MATLAB 2018b platform is used for implementation of the experimental setup. 
Keywords: Teeline Shorthand, Deep learning, Classifiers, Matlab, Accuracy
Scope of the Article:  Deep learning