Analysis of the sign regressor least mean fourth adaptive algorithm

A novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that reduces the computational cost and complexity while maintaining good performance is presented. Expressions are derived for the steady-state excess-mean-square error (EMSE) of the SRLMF algorithm in a stationary environment. A sufficient condition for the convergence in the mean of the SRLMF algorithm is derived. Also, expressions are obtained for the tracking EMSE of the SRLMF algorithm in a nonstationary environment, and consequently an optimum value of the step-size is obtained. Moreover, the weighted variance relation has been extended in order to derive expressions for the mean-square error (MSE) and the mean-square deviation (MSD) of the proposed algorithm during the transient phase. Computer simulations are carried out to corroborate the theoretical findings. It is shown that there is a good match between the theoretical and simulated results. It is also shown that the SRLMF algorithm has no performance degradation when compared with the least mean fourth (LMF) algorithm. The results in this study emphasize the usefulness of this algorithm in applications requiring reduced implementation costs for which the LMF algorithm is too complex

ORAN: a basis for an Arabic OCR system

We present a system called ORAN (offline recognition of Arabic characters and numerals). This system is based on a method called modified MCR (minimum covering run) expression for document images. Using the correspondence between binary images and bipartite graphs, the MCR expression can be found by constructing a minimum covering or maximum matching in the corresponding graph. We use the structural information obtained from this expression to describe the character strokes according to some extracted features. These are obtained after a zoning scheme, where the baseline is detected and the line of text divided into four zones. Reference prototypes for the system are built according to a structural description of characters in some model documents. By this method, we overcome the problem of segmentation that is inherent to Arabic characters, even when they are machine printed or typed. Simple matching of the candidate characters to reference prototypes is performed. A recognition rate of more than 97% is achieved

Single base station mobile-based location estimation technique

In Mobile cellular communications, the location of the users is one of the most important things especially in recent years. The satellite positioning system which is named as Global Positioning System (GPS) is a sufficient and reliable technique for coordinating services.However, there are several techniques which can estimate the location of the user based on certain number of base stations (BS) which are depending on some parameters like multi-path components, reflections, diffractions, ducting, scattering ...etc. This paper discusses several scenarios of wireless mobile location estimation using only one single base-station by utilizing the environment around the subscribers and the base station.A site of Al-Dhahran city in KSA forms the base map for the proposed scenarios and proper solutions are suggested accordingly.The simulation results performance of the approaches used are complying with the FCC standard readings

Adaptive dissection based subword segmentation of printed Arabic text

Numerous segmentation and recognition techniques have been proposed in literature for Arabic OCR system. Correct and efficient segmentation of Arabic text into characters is considered to be a fundamental problem. While OCR systems for other languages do not need segmentation for printed text for successful recognition, it is essential to design robust and powerful segmentation algorithms or employ segmentation free recognition schemes for printed Arabic text. Even more, in recognition of handwritten characters, segmentation is considered to be indispensable. Most of current segmentation technique suffers from over segmentation and under segmentation in addition to not being adaptive in nature. In this paper, we have proposed a new sub-word segmentation scheme, which is independent of font size and font type

Adaptive dissection based subword segmentation of printed Arabic text

Numerous segmentation and recognition techniques have been proposed in literature for Arabic OCR system. Correct and efficient segmentation of Arabic text into characters is considered to be a fundamental problem. While OCR systems for other languages do not need segmentation for printed text for successful recognition, it is essential to design robust and powerful segmentation algorithms or employ segmentation free recognition schemes for printed Arabic text. Even more, in recognition of handwritten characters, segmentation is considered to be indispensable. Most of current segmentation technique suffers from over segmentation and under segmentation in addition to not being adaptive in nature. In this paper, we have proposed a new sub-word segmentation scheme, which is independent of font size and font type

A novel approach for skew estimation of document images in OCR system

Optical character recognition (OCR) is an area which has always received special attention. OCR systems are typically built on the strategy of divide and conquer, rather than recognizing documents at one go. They utilize several stages during the course of recognition. There have been many stages in a typical OCR system, preprocessing stage in considered to be indispensable. An input image or information need to be normalized and converted into format acceptable by OCR system. OCR systems typically assume that documents were printed with a single direction of the text and that the acquisition process did not introduce a relevant skew. Practically this assumption is not very strong and printed document could be skewed at some angle with horizontal axis. In this paper, we have proposed a new technique for skew estimation of image document. In the proposed scheme, multiscale properties of an image are utilized together with principal component analysis to estimate the orientation of principal axis of clustered data

PC based offline Arabic text recognition system

Character recognition systems can contribute tremendously to the advancement of automation process and can improve the interaction between man and machine in many applications. In this paper we describe a PC based system for offline recognition of Arabic characters and numerals. The system is based on expressing the machine printed Arabic alpha-numeric text in terms of strokes obtained by modified MCR Expression [Chinveerapphan, S. et al., Apr. 1995]. The system is implemented on a PIII machine in visual programming language under Windows environment

An approach to offline Arabic character recognition using neural networks

Character recognition system can contribute tremendously towards the advancement of automation process and can be useful in many other applications such as Data Entry, Check Verification etc. This paper presents a technique for the automatic recognition of Arabic Characters. The technique is based on Neural Pattern Recognition Approach. The main features of the system are preprocessing of the text, segmentation of the text to individual characters, Feature extraction using centralized moments technique and recognition using RBF Network. The system is implemented in Java Programming Language under Windows Environment. The System is designed for a single font multi size character set

PC based offline Arabic text recognition system

Character recognition systems can contribute tremendously to the advancement of automation process and can improve the interaction between man and machine in many applications. In this paper we describe a PC based system for offline recognition of Arabic characters and numerals. The system is based on expressing the machine printed Arabic alpha-numeric text in terms of strokes obtained by modified MCR Expression [Chinveerapphan, S. et al., Apr. 1995]. The system is implemented on a PIII machine in visual programming language under Windows environment

License plate recognition system

License Plate recognition (LPR) system is a key to many traffic related applications such as road traffic monitoring or parking lots access control. This paper proposes an automatic license plate recognition system for Saudi Arabian license plates. The system presents an algorithm for the extraction of license plate and segmentation of characters. Recognition is done using template matching. However the proposed work seems to be the first attempt towards the recognition of Saudi Arabian license plates. The performance of the system has been investigated on real images of about 710 vehicles captured under various illumination conditions. Recognition of about 96% shows that the system is quite efficient