Arabic calligraphy, typewritten and handwritten using optical character recognition (OCR) system

Al-barhamtoshy, assanin M.; Kamal, M; Hany,; Mohamed, Shaimaa G. A.; Abdo, Sherif Mahdi; Rashwan, Mohsen

doi:10.21786/bbrc/12.2/11

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…However, this method posed a challenge of line overlap. Consequently, during smearing, two separate lines might merge, causing the segmentation of two lines into one [19]. Fig.…”

Section: The Adopted Methods For Arabic Text-lines Segmentationmentioning

confidence: 99%

A Survey on Text-Line Segmentation in Arab Historical Manuscripts

Djaghbellou,

Attıa,

Bouzıane

2024

International Journal of Informatics and Applied Mathematics

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The segmentation process entails dividing or decomposing the entire document image into segments or lines. This technique serves as a fundamental step in developing any writing or optical character recognition system. However, numerous existing segmentation schemes encounter challenges when dealing with specific script styles, like ancient or historical Arabic writing found in ancient manuscripts. which possesses unique characteristics. These characteristics include inclined text lines, overlapping letters, diacritic marks, decorative elements, variable letter forms, and ligatures (combinations of two or more letters merged to form a single connected shape).Thus, in this paper we present a thorough survey of the field. The survey is composed of two segments. The first segment provides a concise overview of the historical Arabic documents. The second, which serves as the primary segment, focuses on the crucial step of handwritten document recognition, specifically segmentation. A detailed and systematic overview of the various approaches to segmentation, including different levels, employed for extracting handwritten Arabic text-lines, is outlined. Subsequently, a literature study is conducted to review and analyze proposed works in this area.

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“…However, this method posed a challenge of line overlap. Consequently, during smearing, two separate lines might merge, causing the segmentation of two lines into one [19]. Fig.…”

Section: The Adopted Methods For Arabic Text-lines Segmentationmentioning

confidence: 99%

A Survey on Text-Line Segmentation in Arab Historical Manuscripts

Djaghbellou,

Attıa,

Bouzıane

2024

International Journal of Informatics and Applied Mathematics

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“…Projection profiles, Hough transform, and nearest neighboring are used in skew estimation by calculate the skewing angle of the image [8,9], as shown in Fig 18 . Other technique estimated lines in the image and obtained skewing angle. Also, borderlines (if exist) is used to detect skewing angle using run-length algorithm [15]. Sauvola, Otsu and Niblak algorithms are the most methods used to binarize Arabic and Latin documents [16].…”

Section: Evaluation Of the Proposed Modelmentioning

confidence: 99%

“…Noise removing and borderline from old documents is tested in our work. The connected component is used to remove the page frame [15,16].…”

Section: Evaluation Of the Proposed Modelmentioning

confidence: 99%

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Arabic Documents Information Retrieval for Printed, Handwritten, and Calligraphy Image

et al. 2021

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This paper presents a new computational backend model that support Arabic document information retrieval (ADIR) as a dataset and OCR services. Therefore, different services that support document analysis, retrieving, processing including dataset preparation, and recognition will be discussed. Consequently, ADIR services provide general functions of the Arabic OCR to compose large number of other services in the OCR domain. Furthermore, the proposed work can provide accessing different methods of document layout analysis with a platform where they can share and handle such methods (services) without any setup requirements. One of the used datasets composed from 16,800 Arabic letters written by 60 writers. Each writer wrote each letter from Alif to Ya 10 times in two forms. The forms were scanned at 3000DPI resolution and is segmented in two sets: training set with 13,440 letters for 48 images per class label, and testing set with 3,360 letters to 120 images per class label Convolutional neural network (CNN) is used and adapted for Arabic handwritten letters classification. In an experimental test, we showed that our results outperform 100% classification accuracy rate on testing images. Therefore, the ADIR services provide a "service description", which include an interface and a server's URL. The interface allows communication process between clients and services. Although, in this article we evaluate IR results and compared with respect to corrected equivalent.

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An Arabic Manuscript Regions Detection, Recognition and Its Applications for OCRing

Al-Barhamtoshy

Jambi

Rashwan

et al. 2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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The problem of region of interest (RoI) in document layout analysis and document recognition has recently become an essential topic in OCR'ing systems. Arabic manuscript layout analysis and OCRing recognition using language detection, document category, and region of interest (RoI) with Keras and TensorFlow are terms of the state-of-the-art that should be investigated. This paper investigates the problem of Arabic manuscript recognition problems with respect to in OCR'ing-based recognition. A new framework architecture, which integrates “Fast Gradient Sign Method (FGSM)” using Keras and TensorFlow with adversarial image generation during training procedure is proposed. Also, the paper tries to improve the OCRing accuracy of the image enhancement, alignment, layout analysis and recognition using deep learning in multilingual system. Region of Interests (RoIs) detections will be performed using a custom trained deep learning model using bounding box regression with Keras and TensorFlow. This topic investigates an extend of page segmentation method (PSM) to enhance OCRing parameter modes, and enhances Arabic OCRing system accuracy from reinforcement strategy. Therefore, the paper achieves a significant improvement of OCRing results due to the three parameters: language identification, document category, and RoI types (Table, Title, Paragraph, figure, and list). This model is based on “region proposal algorithm” as a basis of CNN object detectors to find the number of the RoIs. Therefore, the proposed framework performs three distinctive tasks: (1) CNN architecture for adversarial training, (2) An implementation of the FGSM with Keras and TensorFlow, and (3) An adversarial training script implementation with the CNN and the FGSM method. The experiments on Arabic manuscript dataset including Arabic text, English/Arabic documents, and Latin digits’ datasets, demonstrate the accuracy of the proposed method. Moreover, the proposed framework performs well and succeeded in defending against adversarial attacks or adversarial images. The experimental results on our collected dataset illustrate the novelty of our proposed framework over the other existing PSM methods to be extended and updated to improve the quality of the OCRing system. The results show that the influence of PSM after expanding using the RoI types, language ID, and document/manuscript category can improve the OCRing accuracy. Also, the experimental results show significant performance by the new framework model with accuracy reached to 99% compared to relative methods.

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Arabic calligraphy, typewritten and handwritten using optical character recognition (OCR) system

Cited by 3 publications

References 5 publications

A Survey on Text-Line Segmentation in Arab Historical Manuscripts

A Survey on Text-Line Segmentation in Arab Historical Manuscripts

Arabic Documents Information Retrieval for Printed, Handwritten, and Calligraphy Image

An Arabic Manuscript Regions Detection, Recognition and Its Applications for OCRing

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