Bilal Bataineh - Academia.edu (original) (raw)

Papers by Bilal Bataineh

Research paper thumbnail of A Proposed Arabic Handwritten Text Normalization Method

Journal of Ict Research and Applications, Nov 1, 2013

Research paper thumbnail of Towards a Student-Centered learning Paradigm in a Virtual environment

International Conference on E-Business, Enterprise Information Systems, E-Government, and Outsourcing, 2007

Research paper thumbnail of Edge direction matrixes-based local binar patterns descriptor for shape pattern recognition

Shapes and texture image recognition usage is an essential branch of pattern recognition. It is m... more Shapes and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from images via human knowledge and works. Local Binary Pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. However, edge direction matrixes (EDMS) only apply global invariant descriptor which employs first and secondary order relationships. The main idea behind this methodology is the need of improved recognition capabilities, a goal achieved by the combinative use of these descriptors. This collaboration aims to make use of the major advantages each one presents, by simultaneously complementing each other, in order to elevate their weak points. By using multiple classifier approaches such as random forest and multi-layer perceptron neural network, the proposed combinative descriptor are compared with the state of the art...

Research paper thumbnail of Skeletonization Algorithm for Binary Images

Procedia Technology, 2013

Research paper thumbnail of Text Normalization Framework for Handwritten Cursive Languages by Detection and Straightness the Writing Baseline

Procedia Technology, 2013

Research paper thumbnail of An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Pattern Recognition Letters, 2011

Research paper thumbnail of A novel statistical feature extraction method for textual images: Optical font recognition

Expert Systems with Applications, 2012

Research paper thumbnail of A Novel Baseline Detection Method of Handwritten Arabic-Script Documents Based on Sub-Words

Communications in Computer and Information Science, 2013

Research paper thumbnail of Adaptive Thresholding Methods for Documents Image Binarization

Binarization process is easy when applying simple thresholding method onto good quality image. Ho... more Binarization process is easy when applying simple thresholding method onto good quality image. However, this task becomes difficult when it deals with degraded image. Most current binarization methods involve complex algorithm and less ability to recover important information from a degradation image. We introduce an adaptive binarization method to overcome the state of the art. This method also aims to solve the problem of the low contrast images and thin pen stroke problems. It can also enhance the effectiveness of solving all other problems. As well as, it does not need to specify the values of the factors manually. We compare the proposed method with known thresholding methods, which are Niblack, Sauvola, and NICK methods. The results show that the proposed method gave higher performance than previous methods.

Research paper thumbnail of Generating an Arabic Calligraphy Text Blocks for Global Texture Analysis

Research paper thumbnail of Arabic calligraphy recognition based on binarization methods and degraded images

Optical Font Recognition is one of the main challenges in this time. The available methods of opt... more Optical Font Recognition is one of the main challenges in this time. The available methods of optical font recognition are deal with the recent documents and fonts types. However, there are neglected in dealing with the historical and regarded documents. Moreover, they have neglected languages that are not belong into Asian or Latin. Regarding to those types of documents, we proposed a new framework of optical font recognition for Arabic calligraphy. We enhance binarization method based on previous works. By introducing that, we achieve better quality images at the preprocessing stage. Then we generate text block before passing mailing to post-processing stages. Then, we extract the features based on edge direction matrixes. In the classification stage, we apply backpropagation neural network to identify the font type of the calligraphy. We observe that our proposal method achieve better performance in both preprocessing and post processing.

Research paper thumbnail of Character recognition based on global feature extraction

Abstract This paper presents a enhanced feature extraction method which is a combination and sele... more Abstract This paper presents a enhanced feature extraction method which is a combination and selected of two feature extraction techniques of Gray Level Co occurrence Matrix (GLCM) and Edge Direction Matrixes (EDMS) for character recognition purpose. It is apparent that one of the most important steps in a character recognition system is selecting a better feature extraction technique, while the variety of method makes difficulty for finding the best techniques for character recognition. The dataset of images that has been applied to ...

Research paper thumbnail of A Statistical Global Feature Extraction Method for Optical Font Recognition

The study of optical font recognition has becoming more popular nowadays. In line to that, global... more The study of optical font recognition has becoming more popular nowadays. In line to that, global analysis approach is extensively used to identify various font type to classify writer identity. Objective of this paper is to propose an enhanced global analysis method. Based on statistical analysis of edge pixels relationships, a novel method in feature extraction for binary images has proposed. We test the proposed method on Arabic calligraphy script image for optical font recognition application. We classify those images using Multilayer Network, Bayes network and Decision Tree classifiers to identify the Arabic calligraphy type. The experiments results shows that our proposed method has boost up the overall performance of the optical font recognition.

Research paper thumbnail of A Proposed Arabic Handwritten Text Normalization Method

Journal of Ict Research and Applications, Nov 1, 2013

Research paper thumbnail of Towards a Student-Centered learning Paradigm in a Virtual environment

International Conference on E-Business, Enterprise Information Systems, E-Government, and Outsourcing, 2007

Research paper thumbnail of Edge direction matrixes-based local binar patterns descriptor for shape pattern recognition

Shapes and texture image recognition usage is an essential branch of pattern recognition. It is m... more Shapes and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from images via human knowledge and works. Local Binary Pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. However, edge direction matrixes (EDMS) only apply global invariant descriptor which employs first and secondary order relationships. The main idea behind this methodology is the need of improved recognition capabilities, a goal achieved by the combinative use of these descriptors. This collaboration aims to make use of the major advantages each one presents, by simultaneously complementing each other, in order to elevate their weak points. By using multiple classifier approaches such as random forest and multi-layer perceptron neural network, the proposed combinative descriptor are compared with the state of the art...

Research paper thumbnail of Skeletonization Algorithm for Binary Images

Procedia Technology, 2013

Research paper thumbnail of Text Normalization Framework for Handwritten Cursive Languages by Detection and Straightness the Writing Baseline

Procedia Technology, 2013

Research paper thumbnail of An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Pattern Recognition Letters, 2011

Research paper thumbnail of A novel statistical feature extraction method for textual images: Optical font recognition

Expert Systems with Applications, 2012

Research paper thumbnail of A Novel Baseline Detection Method of Handwritten Arabic-Script Documents Based on Sub-Words

Communications in Computer and Information Science, 2013

Research paper thumbnail of Adaptive Thresholding Methods for Documents Image Binarization

Binarization process is easy when applying simple thresholding method onto good quality image. Ho... more Binarization process is easy when applying simple thresholding method onto good quality image. However, this task becomes difficult when it deals with degraded image. Most current binarization methods involve complex algorithm and less ability to recover important information from a degradation image. We introduce an adaptive binarization method to overcome the state of the art. This method also aims to solve the problem of the low contrast images and thin pen stroke problems. It can also enhance the effectiveness of solving all other problems. As well as, it does not need to specify the values of the factors manually. We compare the proposed method with known thresholding methods, which are Niblack, Sauvola, and NICK methods. The results show that the proposed method gave higher performance than previous methods.

Research paper thumbnail of Generating an Arabic Calligraphy Text Blocks for Global Texture Analysis

Research paper thumbnail of Arabic calligraphy recognition based on binarization methods and degraded images

Optical Font Recognition is one of the main challenges in this time. The available methods of opt... more Optical Font Recognition is one of the main challenges in this time. The available methods of optical font recognition are deal with the recent documents and fonts types. However, there are neglected in dealing with the historical and regarded documents. Moreover, they have neglected languages that are not belong into Asian or Latin. Regarding to those types of documents, we proposed a new framework of optical font recognition for Arabic calligraphy. We enhance binarization method based on previous works. By introducing that, we achieve better quality images at the preprocessing stage. Then we generate text block before passing mailing to post-processing stages. Then, we extract the features based on edge direction matrixes. In the classification stage, we apply backpropagation neural network to identify the font type of the calligraphy. We observe that our proposal method achieve better performance in both preprocessing and post processing.

Research paper thumbnail of Character recognition based on global feature extraction

Abstract This paper presents a enhanced feature extraction method which is a combination and sele... more Abstract This paper presents a enhanced feature extraction method which is a combination and selected of two feature extraction techniques of Gray Level Co occurrence Matrix (GLCM) and Edge Direction Matrixes (EDMS) for character recognition purpose. It is apparent that one of the most important steps in a character recognition system is selecting a better feature extraction technique, while the variety of method makes difficulty for finding the best techniques for character recognition. The dataset of images that has been applied to ...

Research paper thumbnail of A Statistical Global Feature Extraction Method for Optical Font Recognition

The study of optical font recognition has becoming more popular nowadays. In line to that, global... more The study of optical font recognition has becoming more popular nowadays. In line to that, global analysis approach is extensively used to identify various font type to classify writer identity. Objective of this paper is to propose an enhanced global analysis method. Based on statistical analysis of edge pixels relationships, a novel method in feature extraction for binary images has proposed. We test the proposed method on Arabic calligraphy script image for optical font recognition application. We classify those images using Multilayer Network, Bayes network and Decision Tree classifiers to identify the Arabic calligraphy type. The experiments results shows that our proposed method has boost up the overall performance of the optical font recognition.