An efficient multiple-classifier system for Arabic calligraphy style recognition

Kaoudja, Zineb; Kherfi, Mohammed Lamine; Khaldi, Belal

doi:10.1109/icnas.2019.8807829

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…Other datasets in literature targeted calligraphy style classification by focusing on the style classification alone such as the dataset by Kaoudja et al's (2019). Kaoudja et al (2019) Kufi. Their dataset consists of 267 images divided evenly across the three calligraphy types.…”

Section: Related Workmentioning

confidence: 99%

HICMA: The Handwriting Identification for Calligraphy and Manuscripts in Arabic Dataset

Ismail,

Kamel,

Mahmoud

2023

Proceedings of ArabicNLP 2023

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Arabic is one of the most globally spoken languages with more than 313 million speakers worldwide. Arabic handwriting is known for its cursive nature and the variety of writing styles used. Despite the increase in effort to digitize artistic and historical elements, no public dataset was released to deal with Arabic text recognition for realistic manuscripts and calligraphic text. We present the Handwriting Identification of Manuscripts and Calligraphy in Arabic (HICMA) dataset as the first publicly available dataset with real-world and diverse samples of Arabic handwritten text in manuscripts and calligraphy. With more than 5,000 images across five different styles, the HICMA dataset includes image-text pairs and style labels for all images. We further present a comparison of the current state-of-the-art optical character recognition models in Arabic and benchmark their performance on the HICMA dataset, which serves as a baseline for future works. Both the HICMA dataset and its benchmarking tool are made available to the public under the CC BY-NC 4.0 license in the hope that the presented work opens the door to further enhancements of complex Arabic text recognition. 1 The dataset is a combination of 3 subsets containing each 2,240, 1,000 and 2,000 characters

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Section: Related Workmentioning

confidence: 99%

HICMA: The Handwriting Identification for Calligraphy and Manuscripts in Arabic Dataset

Ismail,

Kamel,

Mahmoud

2023

Proceedings of ArabicNLP 2023

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“…The finding is that the best performance has been yielded by the BSIF descriptor with the SVM classifier [15] suppose a new approach for developing a method for generating Arabic handwriting by testing 7 types of Arabic calligraphy in the work of [16] the author proposed a new framework of optical font recognition for Arabic calligraphy by enhancing the binarization method the [17] present computational abstractions for generating and manipulating calligraphic compositions systematic by s within an interacting environment [18] suppose multi-classifier decisions as based off or Arabic-calligraphy style classification. The [19] used a new technique by define the three important coordinates in the image of each character and then translates it into triangle geometry style.…”

Section: Literature Surveymentioning

confidence: 99%

Fast learning neural network based on texture for Arabic calligraphy identification

Hussein

2021

IJEECS

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<span id="docs-internal-guid-5c723154-7fff-a7b2-3582-b7c2920a9921"><span>Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article proposes using four types of features and a single hidden layer neural network for training on Arabic calligraphy and predicting the type of calligraphy that is used. For neural networks, we compared the case of non-connected input and output layers in extreme learning machine ELM and the case of connected input-output layers in FLN. The prediction accuracy of fast learning machine FLN was superior comparing ELM that showed a variation in the obtained accuracy. </span></span>

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“…On one hand, the approach proposed in the work of Zhang et al (2016) [16] relies totally on finding the semantic relatedness among presegmented regions based on a wide range of handcrafted features [86,87]. By understanding the logic that connects different concepts, the system became able to learn concepts regardless of their narrow use.…”

Section: Regiomentioning

confidence: 99%

“…range of handcrafted features [86,87]. By understanding the logic that connects different concepts, the system became able to learn concepts regardless of their narrow use.…”

Section: Regiomentioning

confidence: 99%

Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation

et al. 2021

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Automatic image annotation is an active field of research in which a set of annotations are automatically assigned to images based on their content. In literature, some works opted for handcrafted features and manual approaches of linking concepts to images, whereas some others involved convolutional neural networks (CNNs) as black boxes to solve the problem without external interference. In this work, we introduce a hybrid approach that combines the advantages of both CNN and the conventional concept-to-image assignment approaches. J-image segmentation (JSEG) is firstly used to segment the image into a set of homogeneous regions, then a CNN is employed to produce a rich feature descriptor per area, and then, vector of locally aggregated descriptors (VLAD) is applied to the extracted features to generate compact and unified descriptors. Thereafter, the not too deep clustering (N2D clustering) algorithm is performed to define local manifolds constituting the feature space, and finally, the semantic relatedness is calculated for both image–concept and concept–concept using KNN regression to better grasp the meaning of concepts and how they relate. Through a comprehensive experimental evaluation, our method has indicated a superiority over a wide range of recent related works by yielding F1 scores of 58.89% and 80.24% with the datasets Corel 5k and MSRC v2, respectively. Additionally, it demonstrated a relatively high capacity of learning more concepts with higher accuracy, which results in N+ of 212 and 22 with the datasets Corel 5k and MSRC v2, respectively.

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An efficient multiple-classifier system for Arabic calligraphy style recognition

Cited by 6 publications

References 21 publications

HICMA: The Handwriting Identification for Calligraphy and Manuscripts in Arabic Dataset

HICMA: The Handwriting Identification for Calligraphy and Manuscripts in Arabic Dataset

Fast learning neural network based on texture for Arabic calligraphy identification

Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation

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