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Remote Sensing And Image Interpretation

Research in offline Arabic handwriting recognition has increased considerably in the past few years. This is evident from the numerous research results published recently in major journals and conferences in the area of handwriting recognition. Features and classifications techniques utilized in recent research work have diversified noticeably compared to the past. Moreover, more efforts have been diverted, in last few years, to construct different databases for Arabic handwriting recognition. This article provides a comprehensive survey of recent developments in Arabic handwriting recognition. The article starts with a summary of the characteristics of Arabic text, followed by a general model for an Arabic text recognition system. Then the used databases for Arabic text recognition are discussed. Research works on preprocessing phase, like text representation, baseline detection, line, word, character, and subcharacter segmentation algorithms, are presented. Different feature extraction techniques used in Arabic handwriting recognition are identified and discussed. Different classification approaches, like HMM, ANN, SVM, k-NN, syntactical methods, etc., are discussed in the context of Arabic handwriting recognition. Works on Arabic lexicon construction and spell checking are presented in the postprocessing phase. Several summary tables of published research work are provided for used Arabic text databases and reported results on Arabic character, word, numerals, and text recognition. These tables summarize the features, classifiers, data, and reported recognition accuracy for each technique. Finally, we discuss some future research directions in Arabic handwriting recognition.

Offline arabic handwritten text recognition: A Survey

Definitions and basic properties of the Voronoi diagram generalizations of the Voronoi diagram algorithms for computing Voronoi diagrams poisson Voronoi diagrams spatial interpolation models of spatial processes point pattern analysis locational optimization through Voronoi diagrams.

Spatial tessellations. Concepts and Applications of Voronoi diagrams

Towards Accurate Detection of Offensive Language in Online Communication in Arabic

The advancements in pattern recognition has accelerated recently due to the many emerging applications which are not only challenging, but also computationally more demanding, such evident in Optical Character Recognition (OCR), Document Classification, Computer Vision, Data Mining, Shape Recognition, and Biometric Authentication, for instance. The area of OCR is becoming an integral part of document scanners, and is used in many applications such as postal processing, script recognition, banking, security (i.e. passport authentication) and language identification. The research in this area has been ongoing for over half a century and the outcomes have been astounding with successful recognition rates for printed characters exceeding 99%, with significant improvements in performance for handwritten cursive character recognition where recognition rates have exceeded the 90% mark. Nowadays, many organizations are depending on OCR systems to eliminate the human interactions for better performance and efficiency. The field of pattern recognition is a multidisciplinary field which forms the foundation of other fields, as for instance, Image Processing, Machine Vision, and Artificial Intelligence. Therefore, OCR cannot be applied without the help of Image Processing and/or Artificial Intelligence. Any OCR system goes through numerous phases including: data acquisition, preprocessing, feature extraction, classification and post-processing where the most crucial aspect is the preprocessing which is necessary to modify the data either to correct deficiencies in the data acquisition process due to limitations of the capturing device sensor, or to prepare the data for subsequent activities later in the description or classification stage. Data preprocessing describes any type of processing performed on raw data to prepare it for another processing procedure. Hence, preprocessing is the preliminary step which transforms the data into a format that will be more easily and effectively processed. Therefore, the main task in preprocessing the captured data is to decrease the variation that causes a reduction in the recognition rate and increases the complexities, as for example, preprocessing of the input raw stroke of characters is crucial for the success of efficient character recognition systems. Thus, preprocessing is an essential stage prior to feature extraction since it controls the suitability of the results for the successive stages. The stages in a pattern recognition system are in a pipeline fashion meaning that each stage depends on the success of the previous stage in order to produce optimal/valid results. However, it is 1

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Preprocessing Techniques in Character Recognition

"Steganography” is a Greek origin word which means “hidden writing”. Steganography word is classified into two parts: Steganos which means “secret or covered” (where you want to hide the secret messages) and the graphic which means “writing” (text). In this paper, a new Steganography technique is presented, implemented and analyzed. The proposed method hides the secret message based on searching about the identical bits between the secret messages and image pixels values. The proposed method is compared with the LSB benchmarking method. It is implemented to hide the secret message "I will come to see you on the first of June" on two Bmp images, with size (24 x 502 x 333) and (24 x 646 x 165) respectively. The results of the proposed and LSB hiding methods are discussed and analyzed based on the ratio between the number of the identical and the non identical bits between the pixel color values and the secret message values. The proposed method is efficient, simple and fast it robust to attack and improve the image quality, hence it obtained an accuracy ratio of 83%.

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A New Method in Image Steganography with Improved Image Quality

Summary Preprocessing is the most important stage in the Arabic OCR system; it has a direct effect on the reliability and efficiency of the segmentation and feature extraction stages. It is worth mentioning that Arabic language is cursively written, and its characters have between 2 to 4 shapes. An Arabic word likely consists of two or more characters which are connected through an imaginary line called baseline. Detecting baseline is one of the main majorities in preprocessing Arabic OCR system. The baseline can be used for both skew normalization and character segmentation. This paper aims to provide a comprehensive review of the methods proposed by researchers to detect Arabic baseline. The Arabic baseline detection methods are categorized into four methods: (a) based on horizontal projection methods, (b) based on word skeleton method, (c) based on contour tracing method, and (d) based on principle component analysis method. Each of these methods has its own advantages and drawbacks.

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Methods of Arabic Language Baseline Detection - The State of Art

Problem statement: Skew detection and correction is the first step process in the document analysis and understanding processing steps. Correction the skewed scanned document image is very important, because it has a direct effect on the reliability and efficiency of the segmentation and feature extraction stages. The noises and the deviation in the document resolution or types are still the main two challenges facing the Arabic skew detection and correction methods. Approach: The proposed method work involved inscribing the text in the document by an arbitrary polygon and derivation of the baseline from polygon’s centroid. Results: The proposed method was implemented on 150 different scanned Arabic documents, from different sources like journals, textbooks, newspapers and the like in addition to handwritten document, with different resolutions and different fonts and it was obtained an accuracy ratio of 87%. Conclusion: The proposed method was efficient, simple and fast, it was not affected by noise and it was proved their suitability to work with documents with different fonts and documents with different resolutions.

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Skew Detection and Correction Technique for Arabic Document Images Based on Centre of Gravity

Preprocessing is the most important stage in the Arabic OCR system; it has a direct effect on the reliability and efficiency of the segmentation and feature extraction stages. It is worth mentioning that Arabic language is cursively written, and its characters have between two to four shapes. An Arabic word likely consists of two or more characters which are connected through an imaginary line called baseline. Detecting baseline is one of the main majorities in preprocessing Arabic OCR system. The baseline can be used for both skew normalization and character segmentation. In this paper the challenges of the Arabic baseline detection methods are listed and clarified. Also this paper aims to provide a brief comparison between the methods of Arabic baseline detection. The comparison has been done based on each of the natures of the Arabic language written, and the diacritics, such as dots and zigzag, and the word slop, and the subwords found.

A comparative study between methods of Arabic baseline detection

The purpose of this paper is to present a methodology based on Voronoi Diagram for detecting the Arabic handwritten word baseline. In this paper, the definition and background of Voronoi Diagram has been explained. A short survey about the application of Voronoi Diagram on the language recognition fields also presented. Finally, a method is proposed based on the Voronoi Diagram for detecting the Arabic baseline handwritten word. The proposed method expects to be the robust Arabic handwriting words baseline detection algorithms, because the images slope and the diacritics will not affect in the method performance. In addition it will be the first method for detecting the Arabic baseline in zigzag way. The proposed method will implement in the next phase, in order to build a complete Arabic optical character recognition system.

Atallah M. Al-Shatnawi

Papers

A New Method in Image Steganography with Improved Image Quality

Methods of Arabic Language Baseline Detection - The State of Art

Skew Detection and Correction Technique for Arabic Document Images Based on Centre of Gravity

A comparative study between methods of Arabic baseline detection

Detecting Arabic handwritten word baseline using Voronoi Diagram