Handwritten Character Recognition with Artificial Neural Networks

Kouamo, Stephane; Tangha, Claude

doi:10.1007/978-3-642-28765-7_64

Cited by 4 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, multi-class SVM classifiers were employed for the text images classification using the following SVM kernel functions [19]: Artificial Neural Network (ANN): Artificial Neural Network (ANN) is considered as a part of a computing system that is designed to mimic the way of analyzing and processing information by the human brain. Furthermore, an ANN model can be seen as a set of interconnected nodes which communicate together and with the outside using a well-known connections called synapses [20]. In this paper, Multi-layer Perceptron (MLP) ANN is used for holistic classification of the handwritten Arabic text.…”

Section: Text Classificationmentioning

confidence: 99%

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

AL-Shatnawi

Al-Saqqar

Alhusban

2020

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

<p class="0abstract">In this paper, we introduce a multi-stage offline holistic handwritten Arabic text recognition model using the Local Binary Pattern (LBP) technique and two machine-learning approaches; Support Vector Machines (SVM) and Artificial Neural Network (ANN). In this model, the LBP method is utilized for extracting the global text features without text segmentation. The suggested model was tested and utilized on version II of the IFN/ENIT database applying the polynomial, linear, and Gaussian SVM and ANN classifiers. Performance of the ANN was assessed using the Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) training methods. The classification outputs of the herein suggested model were compared and verified with the results obtained from two benchmark Arabic text recognition models (ATRSs) that are based on the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) methods using various normalization sizes of images of Arabic text. The classification outcomes of the suggested model are promising and better than the outcomes of the examined benchmarks models. The best classification accuracies of the suggested model (97.46% and 94.92%) are obtained using the polynomial SVM classifier and the BR ANN training methods, respectively.</p>

show abstract

Section: Text Classificationmentioning

confidence: 99%

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

AL-Shatnawi

Al-Saqqar

Alhusban

2020

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…Fingerprints form a very specific class of models with singular particularity and proven statistical characteristics. Thus, the problems of fingerprint recognition seem to be much more constraining than other classical problems of form recognition (such as the recognition of manuscript characters) where neural networks have already been successfully applied [1] [2] [3]. Faultless authentication of individuals by fingerprints remains one of the main problems of fingerprint recognition.…”

Section: Introductionmentioning

confidence: 99%

Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks

Kouamo¹,

Tangha²,

Kouamo³

2020

JSEA

Self Cite

View full text Add to dashboard Cite

Faultless authentication of individuals by fingerprints results in high false rejections rate for rigorously built systems. Indeed, the authors prefer that the system erroneously reject a pattern when it does not meet a number of predetermined correspondence criteria. In this work, after discussing existing techniques, we propose a new algorithm to reduce the false rejection rate during the authentication-using fingerprint. This algorithm extracts the minutiae of the fingerprint with their relative orientations and classifies them according to the different classes already established; then, make the correspondence between two templates by simple probabilities calculations from a deep neural network. The merging of these operations provides very promising results both on the NIST4 international data reference and on the SOCFing database.

show abstract

“…The digital fingerprint forms a specific class of pattern with a particular singularity and statistics characteristics known. Then, fingerprint recognition seems to be more constraining than the other problem of pattern recognition (like handwritten character recognition) where neural network has been well applied [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning

Kouamo¹,

Tangha²

2016

JILSA

Self Cite

View full text Add to dashboard Cite

Fingerprint recognition is a mature biometric technique for identification or authentication application. In this work, we describe a method based on the use of neural network to authenticate people who want to accede to an automated fingerprint system for E-learning. The idea is to apply back propagation algorithm on a multilayer perceptron during the training stage. One of the advantages of this technique is the use of a hidden layer which allows the network to make comparison by calculating probabilities on template which are invariant to translation and rotation. Results come both from the NIST special database 4 and a local database, and show that a proposed method gives good results in some cases.

show abstract

Handwritten Character Recognition with Artificial Neural Networks

Cited by 4 publications

References 3 publications

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks

Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning

Contact Info

Product

Resources

About