Different character recognition problems have their own specific characteristics. The state-of-art OCR technologies take different recognition approaches, which are most effective, to recognize different types of characters. How to identify character type automatically, then use specific recognition engines, has not brought enough attention among researchers. Most of the limited researches are ...

This paper proposes algorithms to localize vehicle number plates from natural background images, to segment the characters from the localized number plates and to recognize the segmented characters. The reported system is tested on a dataset of 560 sample images captured with different background under various illuminations. The performance accuracy of the proposed system has been calculated at...

This paper proposes a system for recognizing numeric strings from topographical maps, which is composed of the automatic recognition stage and the interactive recognition stage. In this method, uncertain numeric strings extracted through the automatic recognition stage based on topographical map feature only, are confirmed and corrected by the interactive recognition stage. Therefore we can obt...

OCR often performs poorly on degraded documents. One approach to improving performance is to determine a good filter to improve the appearance of the document image before sending it to the OCR engine. Quality metrics have been measured in document images to determine what type of filtering would most likely improve the OCR response for that document image. In this paper those same quality metr...

The document images that are fed into an Optical Character Recognition system, might be skewed. This could be due to improper feeding of the document into the scanner or may be due to a faulty scanner. In this paper, we propose a skew detection and correction method for document images. We make use of the inherent randomness in the Horizontal Projection profiles of a text block image, as the sk...

A powerful and flexible technique to identify, classify and process documents using images from a scanning process is presented. The types of documents can be described to the system as a set of differentiating features in a case base using shape trees. The features are filtered and abstracted from an extremely reduced scanner image of the document. Classification rules are stored with the case...

In the previous chapters, we proposed several features for writer identification, historical manuscript dating and localization separately. In this chapter, we present a summarization of the proposed features for different applications by proposing a joint feature distribution (JFD) principle to design novel discriminative features which could be the joint distribution of features on adjacent p...

Optical Character Recognition (OCR) is a process to recognize the handwritten or printed scanned text with the help of a computer. Segmentation is very important stage of any text recognition system. The problems in segmentation can lead to decrease in segmentation rate and hence recognition rate. A good segmentation technique can improve the recognition rate. This paper deals with the hazards ...

Working with Optical Character Recognition for the printed Gurmukhi Script is a challenging task due to the large number of characters, the sophisticated ways in which they combine, and the complicated result. This paper describes a fast and easy to implement algorithm for detection of bold and italic character in Gurmukhi Script. The algorithm works without recognition of actual character and ...