A neuro-fuzzy inference engine for Farsi numeral characters recognition

Montazer, Gholam Ali; Qahri‐Saremi, Hamed; Khatibi, Vahid

doi:10.1016/j.eswa.2010.02.088

Cited by 24 publications

(8 citation statements)

References 35 publications

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“…A probabilistic neural network (PNN) approach for the recognition of the handwritten Indian numerals [17] based on the center of gravity and a set of vectors to the boundary points of the digit has been presented however Montazer et al [18] proposed a holistic approach using neuro-fuzzy inference engine to recognize the Farsi numeral characters. Finally, Impedovo et al introduced a genetic algorithm based clustering approach using zoning features [19] whereas an adaptive zoning techniques for handwritten digit recognition are presented [20,21] where the features are extracted according to an optimal zoning distribution.…”

Section: Introductionmentioning

confidence: 99%

Novel feature extraction technique for the recognition of handwritten digits

Boukharouba

Bennia

2017

Applied Computing and Informatics

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This paper presents an efficient handwritten digit recognition system based on support vector machines (SVM). A novel feature set based on transition information in the vertical and horizontal directions of a digit image combined with the famous Freeman chain code is proposed. The main advantage of this feature extraction algorithm is that it does not require any normalization of digits. These features are very simple to implement compared to other methods. We evaluated our scheme on 80,000 handwritten samples of Persian numerals and we have achieved very promising results. Ó 2015 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).

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Section: Introductionmentioning

confidence: 99%

Novel feature extraction technique for the recognition of handwritten digits

Boukharouba

Bennia

2017

Applied Computing and Informatics

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“…Some novel configurations of deep neural networks like Convolutional Neural network (CNN), Long Short-term Memory (LSTM)/ Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), etc. have got a lot of appreciation and attention due to their superior learning characteristics and efficient classification performance [14][15][16][17][18]. These networks have also been implemented by various researchers for OCR problems with varying complexity.…”

Section: Introductionmentioning

confidence: 99%

Deep Wavelet Neural Network based Robust Text Recognition for Overlapping Characters

Tripathi¹,

Patheja²

2021

IJACSA

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This paper presents a deep learning based intelligent text recognition system with touching and overlapped characters. The robustness and effectiveness in the proposed model are enhanced through the modified configuration of neural network known as Deep Wavelet Neural Network (DWNN). The capability of deep learning networks to learn efficiently from an unlabeled dataset has attracted the attention of many researchers over the last decade. However, the performance of these networks is subject to the quality of the dataset and invariant image representation. Numerous optical character recognition techniques have also been presented in the recent years, but the overlapped and touching characters have not been addressed much. The nonlinear and uncertain representation of image data in case of overlapped text adds severe complexity in the process of feature extraction and respective learning. The proposed architecture of DWNN uses fast decaying wavelet functions as activation function in place of conventional sigmoid function to cope up with the uncertainties and nonlinearity of the data representation in overlapped text images. It comprises of cascaded layered architecture of translated and dilated versions of wavelets as activation functions for the training and feature extraction at multiple levels. The local transformation and deformation variation in the visual data has also been taken care efficiently through the modified architecture of DWNN. Comprehensive experimental analysis has been performed over various test images to verify the effectiveness of the proposed text recognition system. The performance of the proposed method is assessed with the help of the metrics, namely, estimation error, cost function and accuracy. The proposed approach will be implemented in MATLAB.

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“…With the rapid progress of digital imaging acquisition techniques, text recognition becomes increasingly important. The problems in text recognition seem mainly to be due to segmentation (Aas & Eikvil, 1996;Jain & Bhattacharjee, 1992;Montazer, Saremi, & Khatibi, 2010). The problem of text segmentation lies in effective separation into character foreground and background (e.g.…”

Section: Introductionmentioning

confidence: 99%

Display text segmentation after learning best-fitted OCR binarization parameters

Fernández-Caballero

López

Castillo

2012

Expert Systems with Applications

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In this paper text segmentation in generic displays is proposed through learning the best binarization values for a commercial optical character recognition (OCR) system. The commercial OCR is briefly introduced as well as the parameters that affect the binarization for improving the classification scores. The purpose of this work is to provide the capability to automatically evaluate standard textual display information, so that tasks that involve visual user verification can be performed without human intervention. The problem to be solved is to recognize text characters that appear on the display, as well as the color of the characters' foreground and background. The paper introduces how the thresholds are learnt through: (a) selecting lightness or hue component of a color input cell, (b) enhancing the bitmaps' quality, and (c) calculating the segmentation threshold range for this cell. Then, starting from the threshold ranges learnt at each display cell, the best threshold for each cell is gotten. The input and output data sets for testing the algorithms proposed are described, as well as the analysis of the results obtained.

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A neuro-fuzzy inference engine for Farsi numeral characters recognition

Cited by 24 publications

References 35 publications

Novel feature extraction technique for the recognition of handwritten digits

Novel feature extraction technique for the recognition of handwritten digits

Deep Wavelet Neural Network based Robust Text Recognition for Overlapping Characters

Display text segmentation after learning best-fitted OCR binarization parameters

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