Towards a Complete Character Set Meitei Mayek Handwritten Character Recognition
| Autor: | Sarat Saharia, Yumnam Nirmal, Deena Hijam |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Deep learning 020207 software engineering Character encoding 02 engineering and technology computer.software_genre USable Convolutional neural network Image (mathematics) Numeral system Set (abstract data type) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Character recognition |
| Zdroj: | 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). |
| DOI: | 10.1109/iccubea.2018.8697590 |
| Popis: | Off-line Handwritten Character Recognition (HCR) is the automatic conversion of text in an image into letter codes which are usable within computer and text-processing applications. In order to have a complete HCR system of a script, it is essential to consider all the characters present in the character set of the concerned script. This is essentially important for Indian scripts which have a rich set of characters including modifiers and compound characters. Although it is important to consider all the characters, most works have focused mainly on consonants and numerals of the character sets. This paper reports recognition of complete character set of Meitei Mayek script and the issues and challenges faced. A Convolutional Neural Network (CNN) model is proposed to recognize characters of a fairly large dataset consisting of 38,500 samples. Proposed model is also trained and tested against five mini subsets of the dataset to highlight the complexity in terms of recognition accuracy when complete character set is taken into account. It achieves an accuracy of 93.64% and 92.29% on the dataset of 54 and 55 classes respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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