From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

In this paper an off-line Arabic/Farsi handwritten recognition Algorithm on a subset of Farsi name is proposed. In this system, There is no sub-word segmentation phase. Script database includes 3300 images of 30 Farsi common names. The features are wavelet coefficients extracted from smoothed word image profiles in four standard directions. The Centers of competitive layer of RBF neural network have been determined by combining GA and K_Means clustering algorithm. Weights of supervised layer has been trained by using LMS rule and the distances of feature vector of each sample to the centre of RBF network have been computed based on warping function. Experimental results show advantages of this method in field of handwriting recognition.

Off-line Arabic/Farsi handwritten word recognition using RBF neural network and genetic algorithm

The most important part of a character recognition system is segmenting the characters properly and selecting the best features from the characters. This paper describes a character segmentation method for an ANN based character recognition system which is used for recognition of optically scanned handwritten Assamese character. The segmentation of characters are done using horizontal and vertical projections of the hand written text document. For feature extraction the system extracts the geometric features of the characters which are consist of basic line types that are used in the formation of the character skeleton. The feature vector of the training set generated by this system is used to train the recognition system using ANN.

/pdf/segmentation-of-assamese-handwritten-characters-based-on-5c048227y4.pdf

Segmentation of Assamese Handwritten Characters based on Projection Profiles

In this paper we present a new program (On-line Arabic Language Learning System (OALLS)), that can be used to learn the children how to write the arabic characters and words, then it examine if they can write the characters and the words in correct shape. The program is based on the image processing techniques and it deals with the arabic language characters. In this system we have 40 characters and 400 words, for each character and for each word we have 4 templates and 25 squared values, and the matching prcess based on the intensity of the colors in each field. This program summarized in two main parts : a learning part, and a testing part. The character or the word which is written will be matched with its template and show the result of the test. This program shows a good results and it recognize characters in many epoches with a high accuracy (98.60% for Characters and 96.50% for words), in which the experiments were applied on children with ages between 6-11 years. keywords : Hand Writing Recognition, Arabic Language, Image Processing, Microsoft Agent, Optical Character Recognition.

Hand writing recognition system for teaching children arabic language based on ocr

A new approach for Arabic handwriting recognition is proposed. The proposed method is part of a larger software framework to teach Arabic reading and writing to illiterates. The method is customized to each letter of the Arabic alphabet. The characteristics of each letter are analyzed and the appropriate detection scheme for that letter is then determined. This allows the method to provide feedback to the user on the correctness of the character written. Furthermore, in case of incorrect writing, the method indicates what part of the letter was erroneously written. This feedback feature allows the user to enhance his handwriting the next time he writes the same letter. The target is to combat adult illiteracy in the Arab world by using Information Technology.

Mohammad Amin Abou Harb

Papers

A New Approach for Arabic Offline Handwriting Recognition