Recognition of on-Line Arabic Handwritten Characters Using Structural Features

Al-Taani, Ahmad T.; Al-Haj, Saeed

doi:10.13176/11.217

Cited by 39 publications

(20 citation statements)

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“…As compared to the previous works we find that Aburas [36] system achieved 70% of recognition rate, also Khedher [37] system achieved 73.4% of recognition rate, Taani [38] system achieved 75.3% of recognition rate. We achieved 1% increase in recognition over the Abandah [39] system.…”

Section: %mentioning

confidence: 44%

Off-Line System for the Recognition of Handwritten Arabic Character

Sahloul¹,

Suen²

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Section: %mentioning

confidence: 44%

Off-Line System for the Recognition of Handwritten Arabic Character

Sahloul¹,

Suen²

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…In Al-Taani and Al-Haj [5]an after-diffusion Neural Network method is presented for diagnosing leave pests. It is proven that in order to identify the type and species of a leaf, a post-emission Neural Network and the image of the leaf are enough.…”

Section: Review Of Literaturementioning

confidence: 99%

Untitled

2016

Journal of Asian Scientific Research

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“…The performance of this system attested to the capability of HMM in solving the problem of feature variance. Structural features such as vertical projection, contour, horizontal projection, blobs, strokes, primary segment, secondary segments are used to circumvent the problem of feature variance [11]. Yann, et al [12] addressed the problem of invariance in images using convolutional neural networks, where the actual pixel of the images is used as the input to the network.…”

Section: A Techniques Of Feature Invariance Suppression In Text/ Chamentioning

confidence: 99%

On the Problem of Features Variability in Sequence Learning Problems

Yahaya¹

2015

IJFCC

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Abstract-Sequential learning problems such as speech, cursive handwriting, time series forecasting and protein sequence prediction. Both Speech and cursive handwriting recognition are challenging problems to Pattern recognition systems, in particular speech signal. Some peculiar characteristics of these types of problems are that, the signal or pattern evolves with time, modeling a long time dependencies in this pattern is a major challenge. Hidden Markov models (HMM) have been applied for these types of problems. Due to some obvious shortcomings of HMM, neural networks were also explored and applied as well as their hybrids. The problem of feature variability in sequence learning is still a challenging problem. In this paper, we analyzed the problem, present some methods in feature variance suppression in character recognition, and review some research efforts in modification of neural networks and applications. We proposed a structure for a state-based neural network.Index Terms-Sequence learning, feature variability, neural network.

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Recognition of on-Line Arabic Handwritten Characters Using Structural Features

Cited by 39 publications

References 0 publications

Off-Line System for the Recognition of Handwritten Arabic Character

Off-Line System for the Recognition of Handwritten Arabic Character

Untitled

On the Problem of Features Variability in Sequence Learning Problems

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