KERTAS: dataset for automatic dating of ancient Arabic manuscripts

Adam, Kalthoum; Baig, Asim; Al-Maadeed, Somaya; Bouridane, Ahmed; El-Menshawy, Sherine

doi:10.1007/s10032-018-0312-3

Cited by 26 publications

(25 citation statements)

References 21 publications

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“…Adam et al [34] used ancient Arabic manuscripts to discover and test a good algorithm for manuscripts' age and authors detection. They utilized the "KERTAS" dataset, which consists of more than 2000 images of high-quality scanned ancient Arabic manuscripts.…”

Section: ) Textual-based Hand-crafted Featuresmentioning

confidence: 99%

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Manuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels

Khayyat

Elrefaei

2020

IEEE Access

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The instantaneous search and retrieval of the most relevant images to a specific query image is a desirable application for all digital libraries. The automatic extraction and classification according to the most distinguishable features, is a crucial step to detect the similarities among images successfully. This study introduces a novel approach that utilizes a fusion model for classifying and retrieving historical Arabic manuscripts' images. To accomplish our goal, the images are first classified according to their extracted deep learning visual features utilizing a pre-trained convolutional neural network. Then, the texts written in the manuscripts' images are extracted and pre-processed to classify the images according to their textual features using an optimized bidirectional LSTM deep learning model with attention and batch normalization layers. Finally, both the visual and textual deep learning models are fused at three different fusion-levels named: decision-level, features-level, and score-level. The score-level fusion model resulted in a considerable improvement of each model used individually. Extensive experimentation and evaluation of the proposed fusion method on the collected ancient Arabic manuscripts dataset proved its robustness against other stateof-the-art methods recording 99% classification accuracy and 98% mean accuracy on the top-10 image retrieval.

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Section: ) Textual-based Hand-crafted Featuresmentioning

confidence: 99%

“…From table 14, we notice that seven papers [8], [19], [33], [34], [35] [40], and [42] addressed the retrieval of the Arabic manuscript' images. However, all of them used the handcrafted features.…”

Section: Image Classification and Retrieval According To The Fusion Modelsmentioning

confidence: 99%

Manuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels

Khayyat

Elrefaei

2020

IEEE Access

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“…Adam et al [6] used ancient Arabic manuscripts to discover and test a unique algorithm for manuscripts' age and author's prediction. They utilized the KERTAS dataset, which consists of more than 2000 images of highquality scanned ancient Arabic manuscripts.…”

Section: A Prediction Using Traditional Algorithmsmentioning

confidence: 99%

Towards Author Recognition of Ancient Arabic Manuscripts Using Deep Learning: A Transfer Learning Approach

Khayyat¹,

Elrefaei²

2020

IJCDS

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“…In the upstream side, many researchers provide a dataset to facilitate HWCR research. Some examples are the providing of the Lampung handwritten character dataset [1], historical handwritten digit documents of church records by priests in Sweden [2], and Arabic handwriting from historical manuscripts [3]. The methods and approaches in HWCR have also been applied for various scripts, for instance the Kurdish Text Classification of Sorani dialect [4], Slavic Historical Documents containing Glagolitic and Cyrillic character [5], printed Arabic [6], handwritten Bangla characters from India [7], handwritten Kanji characters [8], offline handwritten Chinese characters [9] and so on.…”

Section: Introductionmentioning

confidence: 99%

Writer Identification of Lampung Handwritten Documents Based on Selected Characters

Junaidi

Trianingsih

Iqbal

2020

khif

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Writer identification is a sub-field in handwriting recognition which its objective is to determine the identity of the writer based on handwriting input. The goal is usually for forensic purposes such as finding the perpetrators of crimes that leave traces of evidence in the form of written messages. In addition, writer identification can also be used to determine the identity of a historical actor if he or she leaves a valuable written artefact. The object of this research is the traditional character of the Lampung region which is so-called Had Lampung by the local community. The traditional character of Lampung consists of 20 main characters and 12 diacritics. Based on selected characters, the writer will be recognized using the Principal Component Analysis (PCA) feature. PCA is one linear feature extraction method of an object in pattern recognition. The PCA algorithm consists of several stages, namely the calculation of the average dataset, the subtraction of the vector dataset with averages, the calculation of covariance, the calculation of eigenvectors and eigenvalues, eigenvector reduction, and the projection of the dataset against reduced eigenvector space. PCA in this paper is used as a feature in image recognition. The dataset utilized in this study is the Lampung Dataset which is a handwritten character recognition (HWCR) dataset. Lampung Dataset consists of 82 Lampung handwritten documents. All Lampung character images in the dataset were extracted from these documents using the connected component extraction algorithm and eventually generated 32,140 images. Furthermore, these images are converted into grayscale images. In this research, as many as 12,500 grayscale images of Lampung handwriting characters were chosen to represent 82 different writers. This data is employed as training and testing data on the proposed method. The highest accuracy of the identification of the writer using this PCA feature is 82.92%, while the lowest accuracy is 28.29%.

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KERTAS: dataset for automatic dating of ancient Arabic manuscripts

Cited by 26 publications

References 21 publications

Manuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels

Manuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels

Towards Author Recognition of Ancient Arabic Manuscripts Using Deep Learning: A Transfer Learning Approach

Writer Identification of Lampung Handwritten Documents Based on Selected Characters

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