A Zone Classification Approach for Arabic Documents using Hybrid Features

Hesham, Amany M.; Abdou, Sherif M.; Badr, Amr; Rashwan, Mohsen; Al-Barhamtoshy, Hassanin M.

doi:10.14569/ijacsa.2016.070722

Cited by 5 publications

(8 citation statements)

References 22 publications

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“…Elanwar et al [16,20,23] proposed various analyses based on SVM (support vector machine) classifiers for extracting six logical labels from book pages. The same classifier was utilized by Alshameri et al [19] and Hesham et al [13,22] for text and non-text segmentation. Another learning technique used for segmentation and classification is neural network classification (Multilayer Perceptron-Back propagation) [9], whereas Ahmed et al [10] used k-means clustering and Gaussian Mixture Modelling (GMM).…”

Section: Arabic Document Analysis Methodsmentioning

confidence: 99%

A Review of Arabic Document Analysis Methods

Bouressace

2022

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)

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Arabic document analysis is essential in geometrical information extraction from complex structures in Arabic documents, which can either be historical or modern. This information can be an organized tree structure containing all the component levels, such as column, paragraph, word, table, figure, and article. In this paper, we provide an analysis of recent works on this topic from various perspectives, describing the most commonly used models on document physical layout detection and document logical structure representations in printed styles, summarizing the limitations of previous approaches, identifying challenges along this line of research, and providing new research directions for future algorithms.

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Section: Arabic Document Analysis Methodsmentioning

confidence: 99%

A Review of Arabic Document Analysis Methods

Bouressace

2022

2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)

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“…Group 1 can be divided into 2 subgroups: (1) Subgroup 1 is documents created on plain paper without an overlaid pattern. There are relevant research topics such as text/non-text classification in online handwritten notes [16], the 2D chemical structures recognition in document images [17], detecting math www.ijacsa.thesai.org equations in scientific document images [18], the Arabic word recognition of historical documents images [19], the Vietnamese character recognition for verifying ID card [20], document zoning for document layout analysis [21], analysis of the structure of the musical document image [22], bibliographic reference extraction [23], extracting text and figure from document images [24,25], document localization in natural scene images [26], and table detection and segmentation in document images [27]. (2) Subgroup 2 is created on plain paper and overlapped patterns.…”

Section: Related Workmentioning

confidence: 99%

The Method of Braille Embossed Dots Segmentation for Braille Document Images Produced on Reusable Paper

Tiendee¹,

Lerdsudwichai²,

Thainimit³

et al. 2022

IJACSA

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Braille is the language of communication for blind and visually impaired people. Braille characters are embossed at points to convey the meaning. Typically, Braille documents can be produced on plain paper. Braille documents can be created on reusable paper, also known as a third-page paper; this reduces the paper cost, allowing more available documents to stimulate learning for blind or visually impaired persons. This research presents a method of Braille embossed dots segmentation for Braille document images produced on reusable paper to support the availability of cheaper learning material. Initially, Braille documents were imported with a calibrated scanner, Braille document image layer separation was then performed. Followed by edge removal, Braille embossed dot recovery, noise removal, and specify the embossed Braille point. This research was conducted by using four scanners, which scanned Braille documents images under four different lighting conditions. For each lighting condition, the Braille document image area was cropped to the desired size, considering the possible event conditions. They were used to create over 200,000 Braille cells, with over 12 billion patterns. When calculating the average performance under all lighting conditions, the values were Precision 1.0000, Recall 0.7817, Accuracy 0.8545, and F-Measure 0.8756. By effectively using Braille embossed dots segmentation, the process of Braille document recognition will also be efficient.

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“…There are subsequent tasks in the preprocessing phase, before the layout analysis and IR take place. These tasks include: binarization, noise removal, skew correction, page and zone segmentation [7,8,9]. Therefore, the following characteristics and challenges should be considered.…”

Section: Related Workmentioning

confidence: 99%

“…It is also needing to separate each region from the others, and to deal with the structure of the page. Further, repeated data of the header and footer for a specific documents' categories need extra processing [9] [10].…”

Section: Related Workmentioning

confidence: 99%

“…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].…”

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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A Zone Classification Approach for Arabic Documents using Hybrid Features

Cited by 5 publications

References 22 publications

A Review of Arabic Document Analysis Methods

A Review of Arabic Document Analysis Methods

The Method of Braille Embossed Dots Segmentation for Braille Document Images Produced on Reusable Paper

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

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