A Hybrid Set of Handwriting Features for Handwritten Recognition
Bramara Neelima K1, S Arulselvi2
1Bramara Neelima K*, Research Scholar, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai, India.
2S Arulselvi, Research Supervisor, Associate Professor, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai, India.
Manuscript received on November 14, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 3888-3891 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7765129219/2019©BEIESP| DOI: 10.35940/ijitee.B7765.129219
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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: Handwriting of each person is unique since each person has their own unique and different style of handwriting. Handwriting verification can be performed in two ways, dynamic and static. The dynamic verification process is the writer dependent whereas the static verification process is the writer independent procedure. The features can be spatial, structural, statistical, geometrical, graphological, and from other feature extraction techniques. In this work, we are considering the combination of multilevel feature set for writer recognition and identification purpose. A dataset of different handwriting samples collected from 100 different writers is used for this experiment. A decision tree classifier with random forest implementation is used for recognition and identification of writer with 98.2% accuracy.
Keywords: Handwritten Document, Writer Recognition, Feature Extraction, Decision Tree Classifier.
Scope of the Article: Pattern Recognition