Indian Handwritten Script Identification System Based on Random Forest Tree Ensembles
Lalit P. Ganorkar1, Dinesh V. Rojatkar2 

1Lalit P. Ganorkar, Department of Electronics Engineering, Government College of Engineering Amravati, Amravati, India.
2Dinesh V. Rojatkar, Department of Electronics Engineering, Government College of Engineering Amravati, Amravati, India.

Manuscript received on 01 March 2019 | Revised Manuscript received on 07 March 2019 | Manuscript published on 30 July 2019 | PP: 2097-2103 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2322078219/19©BEIESP | DOI: 10.35940/ijrte.B2322.078219
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Abstract: The work proposal addresses to introduce a methodology for Indian unconstrained handwritten script identification by practicing distinct features and classifiers. By utilizing classifiers like RF, SVM, k-NN, and LDA for Indian script identification using statistical, geometric, and structural features. To preserve all the information present on handwritten documents such as historical, medieval, inscription, financial administration, public records, government archives, letters, land councils, various agreements, etc. in digitalize form needs textual document processing system (e.g. OCR). To build a precise and productive multi-script/language textual document processing system must have script identification. For this study use, total 1288 (line wise) samples of ten scripts use in India are collected from different persons of different gender, age, education and region (rural or urban). After successful training and testing, 81.8% and 0.252 accuracies and the OOB error rate are achieved by Random Forest respectively. And 77.8%, 73.5%, and 65.5% accuracy is achieved in SVM, k-NN and LDA classifiers respectively.
Index Terms: Handwritten Script Identification, SVM, K-NN, LDA, Random Forest Tree Ensembles (RF).

Scope of the Article: Forest Genomics and Informatics