Segmentation-free Word Spotting for Handwritten Arabic Documents

Khaissidi, Ghizlane; Elfakir, Youssef; Mrabti, Mostafa; Yacoubi, Mounîm El; Chenouni, Driss; Lakhliai, Zakia

doi:10.9781/ijimai.2016.411

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…Applying Support Vector Machine on features extracted by CNN [35] achieved 92.95% recognition rate. This CNN-based features outperformed other SVM approaches when applied on other traditional features [7], [20], [38]. Accordingly, our approach studied the effect of SVM and CNN in the classification phase.…”

Section: G Conclusion and Resultsmentioning

confidence: 91%

“…Input strings were represented by pyramidal histograms of characters (PHOC) [36] to be encoded at different levels. Also, pyramid histogram of oriented gradients (PHoG) [37] outperformed the ordinary histograms of gradients (HoG) [7], [20], [38]. Based on literature [5], [39], derivatives were also computed in recently proposed systems.…”

Section: Features Extractionmentioning

confidence: 99%

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Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

2020

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Recently, deep learning techniques demonstrated efficiency in building better performing machine learning models which are required in the field of offline Arabic handwriting recognition. Our ancient civilizations presented valuable handwritten manuscripts that need to be documented digitally. If we compared between Latin and the isolated Arabic character recognition, the latter is much more challenging due to the similarity between characters, and the variability of the writing styles. This paper proposes a multistage cascading system to serve the field of offline Arabic handwriting recognition. The approach starts with applying the Hierarchical Agglomerative Clustering (HAC) technique to split the database into partially interrelated clusters. The interrelations between the constructed clusters support representing the database as a big search tree model and help to attain a reduced complexity in matching each test image with a cluster. Cluster members are then ranked based on our new proposed ranking algorithm. This ranking algorithm starts with computing Pyramid Histogram of Oriented Gradients (PHoG), and is followed by measuring divergence by Kullback-Leibler method. Eventually, the classification process is applied only to the highly ranked matching classes. A comparative study is made to assess the effect of six different deep Convolution Neural Networks (DCNNs) on the final recognition rates of the proposed system. Experiments are done using the IFN/ENIT Arabic database. The proposed clustering and ranking stages lead to using only 11% of the whole database in classifying test images. Accordingly, more reduced computation complexity and more enhanced classification results are achieved compared to recent existing systems. INDEX TERMS Agglomerative hierarchical clustering, deep convolutional neural network, Kullback-Leibler divergence, offline arabic handwritting recognition, pyramid histogram of gradients.

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Section: G Conclusion and Resultsmentioning

confidence: 91%

Section: Features Extractionmentioning

confidence: 99%

Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

2020

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“…(Fig .6) and (Fig .7), shows that the best mean average precision (81 %) is obtained for 100 codewords. Table 2" shows the mean average precision of the approach proposed, and an ancient approach [12] applied to handwritten Arabic documents.…”

Section: Resultsmentioning

confidence: 99%

Word spotting in handwritten Arabic documents using bag-of-descriptors

Elfakir¹,

Khaissidi²,

Mrabti³

et al. 2016

ces

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This paper presents a query-by-example word spotting in handwritten Arabic documents, based on Scale Invariant Feature Transform (SIFT), without using any text word or line segmentation approach, because any errors affect to the subsequent word representation. First the interest points are automatically extracted from the images using SIFT detector, then, we use SIFT descriptor to represent each interest point in the images. In the end, we represent the image's regions as histogram of visual words. The validate study is conducted under a series of controlled experiments on handwritten Arabic documents images.

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“…The system performance was evaluated on 13,000 images and achieved a 99% recognition rate. Khaissidi et al [61] used HOG to detect and describe features of handwritten scripts from the Ibn Sina dataset. The system achieved a 68.4% recognition rate.…”

Section: Handcrafted Featuresmentioning

confidence: 99%

Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

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This study aims to review the latest contributions in Arabic Optical Character Recognition (OCR) during the last decade, which helps interested researchers know the existing techniques and extend or adapt them accordingly. The study describes the characteristics of the Arabic language, different types of OCR systems, different stages of the Arabic OCR system, the researcher's contributions in each step, and the evaluation metrics for OCR. The study reviews the existing datasets for the Arabic OCR and their characteristics. Additionally, this study implemented some preprocessing and segmentation stages of Arabic OCR. The study compares the performance of the existing methods in terms of recognition accuracy. In addition to researchers' OCR methods, commercial and open-source systems are used in the comparison. The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters. Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment, and few approaches were tested on pagelevel scripts. Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low, under 75% for printed text, and further improvement is needed. Moreover, most of the current approaches are offline OCR systems, and there is no remarkable contribution to online OCR systems.

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Segmentation-free Word Spotting for Handwritten Arabic Documents

Cited by 8 publications

References 9 publications

Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

Word spotting in handwritten Arabic documents using bag-of-descriptors

Arabic Optical Character Recognition: A Review

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