An Effective Approach to Offline Arabic Handwriting Recognition

Alabodi, Jafaar; Li, Xue

doi:10.5121/ijaia.2013.4601

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…It can be seen that our method has similar proposal accuracy to the existing best methods as in [18], and it outperforms all the methods as in [15]. [15] 89.74 Alabodi and Li [17] 93.3 Dreuw et al [21] 96.5 PHOCNET [7] 96.11 Azeem and Ahmed [16] 97.7 Ahmad et al [22] 97.22 Stahlberg and Vogel [18] 97.6 Proposed method 97.9…”

Section: Resultsmentioning

confidence: 68%

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WSNet – Convolutional Neural Networkbased Word Spotting for Arabic and English Handwritten Documents

Hassen¹,

Subramanian²,

Al-Maadeed³

et al. 2022

TEM Journal

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This paper proposes a new convolutional neural network architecture to tackle the problem of word spotting in handwritten documents. A Deep learning approach using a novel Convolutional Neural Network is developed for the recognition of the words in historical handwritten documents. This includes a pre-processing step to re-size all the images to a fixed size. These images are then fed to the CNN for training. The proposed network shows promising results for both Arabic and English and both modern and historical documents. Four datasets – IFN/ENIT, Visual Media Lab – Historical Documents (VML-HD), George Washington and IAM datasets – have been used for evaluation. It is observed that the mean average precision for the George Washington dataset is 99.6%, outperforming other state-of-the-art methods. Historical documents in Arabic are known for being complex to work with; this model shows good results for the Arabic datasets, as well. This indicates that the architecture is also able to generalize well to other languages.

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Section: Resultsmentioning

confidence: 68%

“…Image were skeletonized first, and then each pixel was categorized as either horizontal or vertical. These were then coded into numeric vectors in [17]. A fully connected deep learning model was used in [18] on Arabic datasets.…”

Section: Related Workmentioning

confidence: 99%

WSNet – Convolutional Neural Networkbased Word Spotting for Arabic and English Handwritten Documents

Hassen¹,

Subramanian²,

Al-Maadeed³

et al. 2022

TEM Journal

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“…Submitted: 2018-06-18 ISSN: 2297-623X, Vol. 14, pp [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Accepted: 2018-08-10 doi:10.56431/p-nb4392…”

Section: International Journal Of Engineering and Technologiesmentioning

confidence: 99%

New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method

Fergani

Bennia

2018

IJET

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A new hybrid system of off-line analytical recognition of Arabic handwriting combining a neural network type multi-layer perceptron (MLP) and hidden Markov models (HMM) is presented. We propose a way to cooperate HMM and MLP neural network in a probabilistic architecture taking advantage of both tools dedicated to the recognition of Arabic literal amounts. This description is based on statistical and structural characteristics extraction of the significant character of the handwritten Arabic words, which can be used in the MLP classification module to estimate probabilities used as the observations to perform a recognition by the HMM. The originality of our approach is based on the segmentation into characters taking into account diacritics with the characters that match them. The experiments show the convergence of the global system, even with a random initialization of the neural network. Keywords - Recognition of Arabic handwriting, hidden Markov models, fast K-means, Arabic literal amounts, multi-layer perceptron. * E-mail: khaoula_1190@hotmail.com

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“…In [14] , the authors proposed an off-line Arabic handwriting recognition system. The processing is achieved in three main stages.…”

Section: Handwritten Optical Character Recognitionmentioning

confidence: 99%

“…Dehghan, et al [7] Histograms of freeman chain code (Farah, et al, 2004) [4] (Bennamoun, et al, 2000) [5] (Khorsheed, 2003) [8] (Dehghan, et al, 2001) [7] (Pechwitz, et al, 2003) [9] (Hani, 2006) [12] (Menasri, et al, 2007) [10] (Abdurrazzag, et al, 2007) [15] (Touj, et al, 2005) [11] (Dreuw, et al, 2008) [13] (Jafaar, et al, 2013) [14] (Almuallim, et al, 1987) [16] In the next section we will give an overview on web services composition since we will use this technique to combine (integrate) OCR complementary approaches.…”

Section: Characters 90mentioning

confidence: 99%

Flexible Off-line Arabic Handwritten OCR Based on Web Services

Al-Sulbi¹,

Khemakhem²

2015

JKAU: Comp. IT. Sci.

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Despite the large number of approaches and techniques proposed to solve the Arabic handwriting recognition problems, the corresponding results remain weak. Indeed, intensive experiments revealed that such approaches and techniques are unable to deal properly especially with large quantity of handwritten Arabic documents. The complex morphology of the Arabic writing is mainly behind this weakness. A deep study of some of these existing proposed approaches and techniques revealed fortunately their complementarity. Such a complementarity can be exploited on making them collaborating together in a flexible manner. This flexible collaboration can improve substantially the recognition rate and may be leading consequently to building powerful Arabic handwriting systems. Web services seem to be an adequate technology which can make possible the flexible collaboration of several approaches and techniques to solve certain problems. Consequently, we present first in this paper a comprehensive review of Arabic handwriting recognition commonly known as Arabic Optical Character Recognition (AOCR) approaches and techniques. Then, we present our idea which consists to build AOCR based on the flexible collaboration of two or more complementary approaches and techniques by using web service technology.

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An Effective Approach to Offline Arabic Handwriting Recognition

Abstract: ABSTRACT

Cited by 7 publications

References 8 publications

WSNet – Convolutional Neural Networkbased Word Spotting for Arabic and English Handwritten Documents

WSNet – Convolutional Neural Networkbased Word Spotting for Arabic and English Handwritten Documents

New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method

Flexible Off-line Arabic Handwritten OCR Based on Web Services

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