Recognition of Offline Handwritten Characters using 2D-FFT for English and Hindi Scripts
Panyam Narahari Sastry1, Syed Sameer2, Mohammed Sameer Syed3
1Dr. Panyam Narahari Sastry*, Professor, Department of ECE, Chaitanya Bharathi Institute of Technology (A), Hyderabad, India.
2Syed Sameer, PG, Department of ECE, Chaitanya Bharathi Institute of Technology (A), Hyderabad, India.
3Mohammed Sameer Syed, PG, Department of ECE, Chaitanya Bharathi Institute of Technology (A), Hyderabad, India.
Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1512-1517 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7339049420/2020©BEIESP | DOI: 10.35940/ijeat.D7339.049420
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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 Handwritten Character Recognition has been a challenging task for the past many decades. This is an old application related to the area of pattern recognition. Handwritten character recognition (HCR) can be classified into two types namely, Online and Offline. As per the literature survey, there are no standard databases for HCR    since there are very less number of speakers for any Indian language compared to English. Hence, the database of Indian scripts both for testing and training are to be developed in the laboratory environment. The recognition accuracy for printed / typed characters is more than 99 percent, whereas for the HCR it is around 60 percent. Hence the area of HCR is an open area of research. HCR for Indian languages is at nascent stage compared to English since they contain alphabets and also matra’s / sandhi are complex which make the recognition tougher. The freedom of the scriber in writing the script is also another challenge for achieving the better recognition accuracy. This work describes the handwritten character recognition of both Hindi and English scripts by extracting features using 2D FFT and using the Nearest Neighborhood Classifier. The best recognition accuracy for handwritten character recognition of English and Hindi languages obtained is 70%.
Keywords: 2D FFT, Handwritten Character Recognition, Nearest Neighborhood Classifier, Pattern Recognition.