Arabic handwritten digits recognition based on convolutional neural networks with resnet-34 model
| Autor: | Ahmed A. Hashim, Rasool Hasan Finjan, Ali Salim Rasheed, Mustafa Murtdha |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
Computer Networks and Communications Arabic Process (engineering) Computer science computer.software_genre Convolutional neural network Residual neural network Electrical and Electronic Engineering business.industry Deep learning language.human_language Transfer learning Hardware and Architecture Signal Processing Pattern recognition (psychology) language Convolutional neural networks Handwritten digits recognition Artificial intelligence Transfer of learning business Resnet model computer Natural language processing Information Systems |
| Zdroj: | Indonesian Journal of Electrical Engineering and Computer Science. 21:174 |
| ISSN: | 2502-4760 2502-4752 |
| DOI: | 10.11591/ijeecs.v21.i1.pp174-178 |
| Popis: | Handwritten digits recognition has attracted the attention of researchers in pattern recognition fields, due to its importance in many applications in public real life, such as read bank checks and formal documents which is a continuous challenge in the last years. For this motivation, the researchers created several algorithms in recognition of different human languages, but the problem of the Arabic language is still widespread. Concerning its importance in many Arab and Islamic countries, because the people of these countries speak this language, However, there is still a little work to recognize patterns of letters and digits. In this paper, a new method is proposed that used pre-trained convolutional neural networks with resnet-34 model what is known as transfer learning for recognizing digits in the arabic language that provides us a high accuracy when this type of network is applied. This work uses a famous arabic handwritten digits dataset that called MADBase that contains 60000 training and 1000 testing samples that in later steps was converted to grayscale samples for convenient handling during the training process. This proposed method recorded the highest accuracy compared to previous methods, which is 99.6%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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