Bayram Annanurov
International University, Cambodia
Deep learningHandwriting recognitionMachine learningMinificationFeature selectionArtificial intelligenceIntelligent word recognitionLayer (object-oriented design)Pattern recognitionDowntimeSpare partReliability engineeringOperational availabilityDigitizationTask (project management)UnavailabilitySpare parts managementClassifier (linguistics)Speech recognitionText recognitionLimited accessFeature (machine learning)Computer scienceIntelligent character recognitionArtificial neural networkFeature extractionInteger programmingGoal programmingWriting systemConvolutional neural network
Publications 4
#1Bayram Annanurov (International University, Cambodia)H-Index: 1
#2Norliza Mohd Noor (UTM: Universiti Teknologi Malaysia)H-Index: 13
The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres. Data collected for previous experiments were used in this work. The one-against-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two con...
#2Norliza Mohd Noor (UTM: Universiti Teknologi Malaysia)H-Index: 13
#3Haslaile Abdullah (UTM: Universiti Teknologi Malaysia)H-Index: 7
Last. Bayram Annanurov (International University, Cambodia)H-Index: 1
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Spare parts management of the aircrafts is a part of maintenance planning that requires effective and efficient planning in order to reduce aircraft downtime in maintenance processes. Spare parts unavailability during maintenance is one of the factors that affects operational availability of the aircrafts fleet. This study focused on identifying the current problem affecting operational availability of the aircrafts fleet, developing an optimization model for cost minimization of spare parts and...
#1Bayram Annanurov (UTM: Universiti Teknologi Malaysia)H-Index: 1
#2Norliza Mohd Noor (UTM: Universiti Teknologi Malaysia)H-Index: 13
We propose a model of feature selection for offline handwriting recognition. The targeted area is recognition of Khmer handwritten text. We make use of correlation of features, two dimensional Fourier transformation and Gabor filters. We also pass the reduced data through a distance-based classifier to compare performance of each method. Feature selection is an important step towards improving recognition of handwritten text, especially for alphasyllabary writing systems like Khmer.
1 CitationsSource
#1Bayram Annanurov (Zaman University)H-Index: 1
#2Norliza Mohd Noor (UTM: Universiti Teknologi Malaysia)H-Index: 13
This paper proposes a model for an offline handwritten Khmer character recognition. We make use of two dimensional Fourier transformation for feature selection and feed-forward Artificial Neural Net as classification tool. The recognition system allows using the nature of Khmer writing, which is an example of alphasyllabary (Abugida) writing systems. The recognition of the normalized handwritten images has been performed for comparison purposes. The results suggest that the recognition rate incr...