Recognition of handwritten Arabic text awaits accurate recognition solutions. There are many difficulties facing a good handwritten Arabic recognition system such as unlimited variation in human handwriting, similarities of distinct character shapes, and their position in the word. The typical Optical Character Recognition (OCR) systems are based mainly on three stages, preprocessing, features ...

Automated recognition of unconstrained handwriting continues to be a challenging research task. In addition to the errors caused by image quality, image features, segmentation, and recognition, in this paper we have also explored the influence of image complexity on handwriting recognition and compared humans’ versus machines’ recognition. We describe a new methodology that will exploit the gap...

An automatic feature generation method for handwritten digit recognition is described. Two different evaluation measures, orthogonality and information, are used to guide the search for features. The features are used in a backpropagation trained neural network. Classification rates compare favorably with results published in a survey of high-performance handwritten digit recognition systems. T...

Handwritten character recognition represents a problem that was approached in many ways by the scientists. Although a generally solution for any type of handwritten characters not founded yet, the obtained results give hopes to continue the researches in this field. In this paper, we present a software architecture used in character recognition: Hierarchical Neural Network (HNN) architecture (H...

This paper deals with accuracy improvement of handwritten character recognition by the GLVQ (generalized learning vector quantization). In literature , the way of combining the FDA (Fisher discriminant analysis) and the GLVQ was investigated and evaluated to be effective for handwritten Chinese character recognition employing the minimum Euclidian distance classifier. In this paper, the project...

Two kinds of wavelet features are proposed: (a) Kirsch edge enhancement based 2D wavelets and (b) 2D complex wavelets. The two sets of hybrid features are congregated by combining them with the geometrical features for the recognition of handwritten numerals. Experiments conducted on handwritten numeral recognition and verification show that the two hybrid feature sets can achieve high recognit...

In this paper we consider a feature extraction approach for recognition of handwritten electrical symbols. The symbols are represented as a sequence of points. We apply a feature extraction technique to extract the most important features and then feed them for recognition to a Neural Network. We utilize a Learning Vector Quantization (LVQ) network and show its capability to recognize the symbo...

Recognition of Devanagari scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character. In this paper we have compared SVM and KNN on handwritten as well as on printed character and numerical databa...

With the advancement in the technology the jobs for the human kind is becoming more and more tedious with millions and trillions of documents waiting to be processed. Certainly, such a daunting tasks are next to impossible to be completed manually. Over a period, the things are getting automatically done with the robots or intelligent computer systems. Optical character recognition is one of su...

This paper presents a new unsupervised learning approach with stacked autoencoder (SAE) for Arabic handwritten digits categorization. Recently, Arabic handwritten digits recognition has been an important area due to its applications in several fields. This work is focusing on the recognition part of handwritten Arabic digits recognition that face several challenges, including the unlimited vari...