A Survey: Artificial Neural Network for Character Recognition

Pandya, Mrudang; Jay, Rai

doi:10.1007/978-981-10-0448-3_33

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Generally, OCR systems are capable of achieving better performance for scanned documents or images when compared with the images captured by the cameras. In general, several issues are present in the various OCR systems which suffer from different types of issues such as scene complexity [22], uneven lighting conditions [23], document text skewness [24] and multilingual environment [25] resulting in poor performance of OCR applications.…”

Section: A Issues and Challenges In Ocr Systemsmentioning

confidence: 99%

An Experimental Technique for OCR Line and Word Segmentation using Probability Distribution Estimation

2019

ijrte

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Segmentation is always an important step in designing an Optical Character Recognition (OCR) of any script. In this paper, we focus on the line and word segmentation in typewritten Gurmukhi script documents. In order to perform this task, we consider OCR based methodology where several processing steps are implemented. The typewritten documents suffer from several issues such as noise, skew, and quality of the document. In this work, we present a combined pre-processing scheme where document thresholding and skew detection and correction schemes are implemented where image thresholding is obtained using Niblack’s method and skew correction is carried out using gradient histogram algorithm and uniform orientation is obtained. Later, line segmentation scheme is applied where probability density function is applied to generate the text distribution in the probability map. Here, identifying the relation of the text to the exact line is a challenging task hence, we present a 2D-Gaussian modelling which helps to identify the text boundaries in the x and y direction. The proposed methodology is applied for typewritten Gurmukhi documents and an experimental study is carried out to show that the proposed approach achieves better performance when compared with the existing techniques

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Section: A Issues and Challenges In Ocr Systemsmentioning

confidence: 99%

An Experimental Technique for OCR Line and Word Segmentation using Probability Distribution Estimation

2019

ijrte

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“…Artificial neural networks (ANNs) are very powerful in classification problems, they give a good generalization of knowledge present in the training set [1], they are less sensitive to noise, and they are less vulnerable to the presence of incorrect data. Despite these advantages, neural networks also have a number of disadvantages, such as the initial network architecture, and so it is quite difficult to find the right topology of neural networks [2].…”

Section: Introductionmentioning

confidence: 99%

Symbolic interpretation of artificial neural networks using genetic algorithms

Yedjour¹,

Benyettou²,

Yedjour³

2018

Turk J Elec Eng & Comp Sci

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The knowledge acquired during the learning of artificial neural networks (ANNs) is coded as values in synaptic weights, which makes their interpretations difficult, hence the name of the black box. The aim of this work is to provide a comprehensible interpretation of the ANN's decisions by extracting symbolic rules. We improve the performance of our extraction algorithm by combining the ANN with a genetic algorithm. Misleading rules whose support and confidence values are less than fixed thresholds are removed and, as a result, the comprehensibility is improved. The extracted rules are evaluated and compared with other works. The results show good performance of our proposal in terms of fidelity and accuracy.

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Symbolic interpretation of artificial neural networks based on multiobjective genetic algorithms and association rules mining

Yedjour

Benyettou

2018

Applied Soft Computing

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A Survey: Artificial Neural Network for Character Recognition

Cited by 4 publications

References 2 publications

An Experimental Technique for OCR Line and Word Segmentation using Probability Distribution Estimation

An Experimental Technique for OCR Line and Word Segmentation using Probability Distribution Estimation

Symbolic interpretation of artificial neural networks using genetic algorithms

Symbolic interpretation of artificial neural networks based on multiobjective genetic algorithms and association rules mining

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