Feature selection for Khmer handwritten text recognition

Annanurov, Bayram; Noor, Norliza Mohd

doi:10.1109/eiconrus.2017.7910634

Cited by 2 publications

(5 citation statements)

References 18 publications

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“…Current experiments were based on the same data set and most preprocessing steps [9], [10]. Later, the potential to highly increase the recognition rate of neural networks was explored [21].…”

Section: Methodsmentioning

confidence: 99%

“…Figure 2 shows the development of the Khmer HTR framework. Data collection and preliminary experiments were completed in our previous work [9], [10]. In preliminary experiments, the number of features was reduced by 90% using three independent methods: correlation-based feature selection (CORR), two-dimensional Fourier transform (FT2D, and Gabor filters (GF).…”

Section: Methodsmentioning

confidence: 99%

“…Ye et al [8] developed an online recognition method for printed text in the Khmer, Bangla, and Myanmar alphabets. The amount of work in the field, as well as the nature of the collected data for relevant experiments, describes the current state of the art for Khmer handwritten text recognition (HTR) As an extension of previous experiments [9], [10], current work implemented CNNs for Khmer HTR. A novel, compact model 2+1CNN was proposed to be used alongside the models used in literature (LeNet-5 [1], AlexNet [11], visual geometry group 16 (VGG16), VGG19 [12], ResNet [13]).…”

Section: Introductionmentioning

confidence: 99%

“…Lack of appropriate datasets contributes to its difficulty. Only datasets collected in preliminary experiments [9], [10] were used.…”

Section: Introductionmentioning

confidence: 99%

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A compact deep learning model for Khmer handwritten text recognition

Annanurov

Noor

2021

IJ-AI

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<p>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 oneagainst-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two convolutional layers and one fully connected layer, was proposed. The recognition rate came out to be within 93-98%. The compact model is performed on par with the state-of-theart models. It was discovered that computational capacity requirements usually associated with deep learning can be alleviated, therefore allowing applications under limited computational power.</p>

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Lack of appropriate datasets contributes to its difficulty. Only datasets collected in preliminary experiments [9], [10] were used.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A compact deep learning model for Khmer handwritten text recognition

Annanurov

Noor

2021

IJ-AI

Self Cite

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show abstract

“…This is because both feature extraction methods provide scalability and steerability option to describe an image (Than et al, 2017). Gabor has shown its capability to classify various fields such as lung (Mitani et al, 2000), fingerprint (Lee & Wang, 1999), face (See, Noor, Low, & Liew, 2017) and even hand writing (Annanurov & Noor, 2017). Riesz Transform has shown its potential in classifying diseased lungs (Depeursinge & Rodriguez, 2011) as well as different lung tissues (Cirujeda et al, 2016(Cirujeda et al, , 2015.…”

mentioning

confidence: 99%