In this paper we present a system of Arabic characters recognition dedicated to the automatic reading of ACR (Arabic Character Recognition). The developed system is a Fuzzy classifier: Fuzzy Logic (FL) combined with the Expert System (ES) to extract the topological and the contextual informations of each Print character. This combination is very useful to improve the powerful of Hybrid Intellig...

Neural number recognition as an extension of an Optical Character Recognition (OCR) system requires a unique feature set capturing relevant details of the input profiles. The performance of such a recognition system depends on the feature set. The work deals with the design of a combined character and numeral recognition system using a hybrid feature set applied earlier to the recognition of As...

Abstract Optical Character Recognition (OCR) has become one of the most important techniques in computer vision, given that it can easily obtain information from various images. However, existing OCR cannot recognition Telugu literature characters (Handwritten Golusu Kattu writing) due to a lack datasets and trained deep Convolutional Neural Networks (CNN). Since Kakatiya Empire (12 th 14 centu...

Segmentation is one of the important phases of Optical Character Recognition (OCR) system, which extracts objects of interest from an image. Feature extraction and classification phases of OCR will be more effective, if the techniques selected for segmentation is effective. This paper focuses on to develop a system for handwritten documents containing Kannada script and proposes suitable techni...

The process of transcribing a language represented in its spatial form graphical characters into symbolic representation is called handwriting recognition. Each script has collection or letters, often known as symbols, that all share the same fundamental shapes. Handwriting analysis aims to correctly identify input images before being analysed by various automated systems. Recent research image...

Arabic characters illustrate intricate, multidimensional and cursive visual information. Developing a machine learning system for Arabic character recognition is an exciting research. This paper addresses a neural computing concept for Arabic Optical Character Recognition (OCR). The method is based on local image sampling of each character to a selected feature matrix and feeding these matrices...

This paper discusses the different methods for optical character recognition (OCR), which has been an important field to research from a few decades due its huge necessity to convert paper documents or books in computer readable format. Though Bangla (widely used as Bengali) is one of the top uses language among the other languages, but there is no reliable character recognizer for this. Our wo...

Optical character recognition (OCR) algorithms typically start from a binary label image. The need for a binary image is complicated by the fact that most imaging devices usually produce multiply valued data, a grey scale image. The problem then becomes how to extract the meaningful character data from the grey scale image. Image artifacts such as dirt, variations in background intensity, and i...

Neural networks are often used for pattern recognition. They prove to be a popular choice for OCR (Optical Character Recognition) systems, especially when dealing with the recognition of printed text. In this paper, multilayer perceptrons are used for the recognition of handwritten digits. The accuracy achieved proves that this application is a working prototype that can be further extended int...

Most OCR (Optical Character Recognition) systems developed to recognize texts embedded in multimedia documents segment the text into characters before recognizing them. In this paper, we propose a novel approach able to avoid any explicit character segmentation. Using a multi-scale scanning scheme, texts extracted from videos are first represented by sequences of learnt features. Obtained repre...