Optical character recognition, usually abbreviated to OCR, is that the mechanical or electronic conversion of scanned pictures of written, typed or written text into machine-encoded text. It’s wide used as a style of knowledge entry from some form of original paper data supply, whether or not documents, sales receipts, mail, or any range of written records. It’s a typical technique of digitizin...

Optical character recognition (OCR) is an efficient way of converting scanned image into machine code which can further edit. There are variety of methods have been implemented in the field of character recognition. This paper proposes Optical character recognition by using Template Matching. The templates formed, having variety of fonts and size .In this proposed system, Image pre-processing, ...

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...

In this paper, we present a new optical character recognition (OCR) approach which allows real-time, automatic extraction and recognition of digits in images and videos. Our method relies on active contours in order to robustly extract optical characters from real-world visual scenes. The detected character recognition is based on template matching. Our developed system has shown excellent resu...

The techniques of image processing have been used in optical character recognition (OCR) for a long time. The recognition method evolved from early "pattern recognition" to "feature extraction" recently. The recognition rate is raised from 70% to 90%. But the character by character recognition technique has its limitation. Using language models to assist the OCR system in improving recognition ...

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Whereas optical character recognition (OCR) systems learn to classify single characters; people learn to classify long character strings in parallel, within a single fixation . This difference is surprising because high dimensionality is associated with poor classification learning. This paper suggests that the human reading system avoids these problems because the number of to-be-classified im...

We present a lexicon-free post-processing method for optical character recognition (OCR), implemented using weighted finite state machines. We evaluate the technique in a number of scenarios relevant for natural language processing, including creation of new OCR capabilities for low density languages, improvement of OCR performance for a native commercial system, acquisition of knowledge from a...

This report explores the latest advances in the field of digital document recognition. With the focus on printed document imagery, we discuss the major developments in optical character recognition (OCR) and document image enhancement/restoration in application to Latin and non-Latin scripts. In addition, we review and discuss the available technologies for hand-written document recognition. In...