PHND: Pashtu handwritten numerals database and deep learning benchmark
In this paper we introduce a real Pashtu handwritten numerals dataset (PHND) having 50,000 scanned images and make publicly available for research and scientific use. Although more than fifty million people in the world use this language for written and oral communication, no significant efforts are...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Navarra |
| Repositorio: | Dadun. Depósito Académico Digital de la Universidad de Navarra |
| Idioma: | inglés |
| OAI Identifier: | oai:dadun.unav.edu:10171/67659 |
| Acceso en línea: | https://hdl.handle.net/10171/67659 |
| Access Level: | acceso abierto |
| Palabra clave: | Pashtu handwritten numerals dataset (PHND) Handwriting Pashtu optical character recognition (POCR) Algorithm Deep learning Recognition |
| Sumario: | In this paper we introduce a real Pashtu handwritten numerals dataset (PHND) having 50,000 scanned images and make publicly available for research and scientific use. Although more than fifty million people in the world use this language for written and oral communication, no significant efforts are devoted to the Pashtu Optical Character Recognition (POCR). We present a new approach for Pahstu handwritten numerals recognition (PHNR) based on deep neural networks. We train Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) on high-frequency numerals for feature extraction and classification. We evaluated the performance of the proposed algorithm on the newly introduced Pashtu handwritten numerals database PHND and Bangla language number database CMATERDB 3.1.1. We obtained best recognition rate of 98.00% and 98.64% on PHND and CMATERDB 3.1.1. respectively. |
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