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Recognition of Pashto Handwritten Characters Based on Deep Learning
- Source :
- Sensors, Volume 20, Issue 20, Sensors, Vol 20, Iss 5884, p 5884 (2020), Sensors (Basel, Switzerland)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto&rsquo<br />s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, &ldquo<br />Poha&rdquo<br />for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.
- Subjects :
- Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
deep features fusion
lcsh:Chemical technology
Biochemistry
Convolutional neural network
Article
computer vision
Analytical Chemistry
Set (abstract data type)
Pashto handwritten character recognition
convolutional neural networks
0202 electrical engineering, electronic engineering, information engineering
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
Cursive
Character (computing)
business.industry
Deep learning
Cognitive neuroscience of visual object recognition
deep learning
020206 networking & telecommunications
Pattern recognition
Atomic and Molecular Physics, and Optics
language.human_language
ComputingMethodologies_PATTERNRECOGNITION
language
Pashto
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....2f18bf05d3f7c13c1cd5c66e2f5e4df3
- Full Text :
- https://doi.org/10.3390/s20205884