On developing complete character set Meitei Mayek handwritten character database
| Autor: | Sarat Saharia, Deena Hijam |
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| Rok vydání: | 2021 |
| Předmět: |
Database
Computer science Character encoding computer.software_genre Computer Graphics and Computer-Aided Design Convolutional neural network Random forest Support vector machine Character (mathematics) Handwriting Feature (machine learning) Computer Vision and Pattern Recognition Sample collection computer Software |
| Zdroj: | The Visual Computer. 38:525-539 |
| ISSN: | 1432-2315 0178-2789 |
| DOI: | 10.1007/s00371-020-02032-y |
| Popis: | This paper introduces a large-scale Meitei Mayek handwritten character database. It consists of the complete character set of the script. There are a total of 85,124 character images of 55 character classes with 72,330 and 12,794 images in training and test sets, respectively. The present work focuses on collecting the natural handwriting of individuals by carrying out sample collection in two phases: (a) unconstrained handwriting in the form of answer sheets and classroom notes and (b) tabular forms. A total of nearly 500 individuals have contributed in the development of the database. Recognition of the character images in the database is carried out using different feature descriptors with four popular classifiers, namely KNN, Linear Support Vector Classifier, Random Forest and Support Vector Machine. The paper also proposes a convolutional neural network (CNN) model by enhancing a base CNN architecture by optimally tuning the hyperparameters. Experimental results show that the CNN model can be benchmarked against the concerned database with a test accuracy of 95.56%. |
| Databáze: | OpenAIRE |
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