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Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition
- Source :
- IEEE Access, Vol 9, Pp 18569-18584 (2021)
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers, 2021.
-
Abstract
- One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST).
- Subjects :
- feature learning
Arabic text recognition
Vocabulary
General Computer Science
Computer science
Arabic
media_common.quotation_subject
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
0507 social and economic geography
Arabic text recognition, feature learning, bag of features, sparse auto-encoder, hidden Markov models
02 engineering and technology
computer.software_genre
Discriminative model
Classifier (linguistics)
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
sparse auto-encoder
Hidden Markov model
Cursive
media_common
hidden Markov models
business.industry
Deep learning
05 social sciences
General Engineering
Autoencoder
bag of features
language.human_language
ComputingMethodologies_PATTERNRECOGNITION
Pattern recognition (psychology)
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
language
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
business
lcsh:TK1-9971
050703 geography
computer
MNIST database
Natural language processing
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- IEEE Access, Vol 9, Pp 18569-18584 (2021)
- Accession number :
- edsair.doi.dedup.....b41c679c9cf2bad87273ac9dc1d23fb1