Bilal Bataineh

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

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.

A novel statistical feature extraction method for textual images: Optical font recognition

The binary image is essential to image formats where the textual image is the best example of the binary image representation. Feature extraction is a fundamental process in pattern recognition. In this regard, pattern recognition studies involve document analysis techniques. Optical font recognition is among the pattern recognition techniques that are becoming popular today. In this paper, we propose an enhanced global feature extraction method based on the on statistical analysis of the behavior of edge pixels in binary images. A novel method in feature extraction for binary images has been proposed whereby the behavior of the edge pixels between a white background and a black pattern in a binary image captures information about the properties of the pattern. The proposed method is tested on an Arabic calligraphic script image for an optical font recognition application. To evaluate the performance of our proposed method, we compared it with a gray-level co occurrence matrix (GLCM). We classified the features using a multilayer artificial immune system, a Bayesian network, decision table rules, a decision tree, and a multilayer network to identify which approach is most suitable for our proposed method. The results of the experiments show that the proposed method with a decision tree classifier can boost the overall performance of optical font recognition.

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 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.

Character recognition based on global feature extraction

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 the different feature extraction techniques includes the binary character with different sizes. Experimental results show the better performance of proposed method in compared with GLCM and EDMS method after performing the feature selection with neural network, bayes network and decision tree classifiers

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

Baseline detection is an important process in document image analysis and recognition systems. It is extensively used to many various preprocessing stages such as text normalization, skew correction, characters segmentation, slant and slop correction as well as in feature extraction. in this work, we proposed a new method for baseline detection based on horizontal projection histogram and directions features of subwords skeleton for Arabic script; which form the main component of the text that may consist of at least one letter, in addition of diacritic and dots. The efficiency of the proposed method is has been proven by the experiment’s results on an IFN/ENIT Arabic benchmark dataset.

Adaptive thresholding methods for documents image binarization

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.

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 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.

Adaptive binarization method for degraded document images based on surface contrast variation

AbstractDocument binarization is an important technique in document image analysis and recognition. Generally, binarization methods are ineffective for degraded images. Several binarization methods have been proposed; however, none of them are effective for historical and degraded document images. In this paper, a new binarization method is proposed for degraded document images. The proposed method based on the variance between pixel contrast, it consists of four stages: pre-processing, geometrical feature extraction, feature selection, and post-processing. The proposed method was evaluated based on several visual and statistical experiments. The experiments were conducted using five International Document Image Binarization Contest benchmark datasets specialized for binarization testing. The results compared with five adaptive binarization methods: Niblack, Sauvola thresholding, Sauvola compound algorithm, NICK, and Bataineh. The results show that the proposed method performs better than other methods in all binarization cases.

Arabic-Jawi Scripts Font Recognition Using First-Order Edge Direction Matrix

Document image analysis and recognition (DIAR) techniques are a primary application of pattern recognition. OFR is one of the most important DIAR techniques. The information about font type indicates important information to support human knowledge and other document analysis and recognition techniques. In this paper, a new optical font recognition method for Arabic scripts is proposed based on the First order edge direction matrix, which is an effected simple feature extraction method for binary images. The proposed methods based on several recent methods in pre-processing and feature extraction stages. The performance of the proposed method is compared with the previous OFR methods that based on texture analysis methods in the feature extraction stage. The results show that the proposed method presents the best performance than of other methods in terms of computation time and accuracy.