A Simplified Research for Mathematical Expression Recognition and its Conversion to Speech
Punith Kumar1, T Shreekanth2, Shashank N S3, Sneha S4
1Dr. Punith Kumar, M B Associate Professor, Department of ECE, PESCE, Mandya (Karnataka), India.
2Dr. T Shreekanth, Mysore (Karnataka), India.
3Shashank N S, Department of ECE, SJCE, Mysore (Karnataka), India.
4Sneha S, Department of ECE, SJCE, Mysore (Karnataka), India.
Manuscript received on 19 August 2019 | Revised Manuscript received on 10 September 2019 | Manuscript Published on 17 September 2019 | PP: 1033-1038 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10080882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1008.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The number of visually impaired people appearing for various examination is increasing every year while on the other hand, there are several blind aspirants who are willing to enrich their knowledge through higher studies. Mathematics is one of the key language (subject) for those who are willing to pursue higher studies in science stream. There is a lot of advanced Braille techniques and OCR to speech conversion software’s made available to help visual impaired community to pursue their education but still the number of visually impaired students getting admitted to higher education is less. This is not because most of the data is on paper in the form of books and documents. So, there is a great need to convert information from the physical domain into the digital domain which would help the visually impaired people to read the advanced mathematics text independently. Optical Character Recognition (OCR) systems for mathematics have received considerable attention in recent years due to the tremendous need for the digitization of printed documents. Existing literature reveals that, most of the works concentrated on recognizing handwritten mathematical symbols and some works revolve around complex algorithms. This paper proposes a simple, yet efficient approach to develop an OCR system for mathematics and its conversion to speech. For Mathematical symbol recognition, Skin and Bone algorithm is proposed, which proved its efficiency on a variety of data set. The proposed methodology has been tested on 50 equations comprising various symbols such as integral, differential, square, square root and currently achieving recognition rate of 92%.
Keywords: Skin and Bone Algorithm, Connected Component Labelling, Projection Profile, Segmentation, 2D Correlation.
Scope of the Article: Signal and Speech Processing