Contents I. Introduction
The value of a sheet of paper in enabling communication between people cannot be overstated. Important information is usually written on paper and then retrieved at a later time. In the form of a handwritten script, paper is still a convenient and practical technique for storing information. So we are trying to simulate intelligent behavior by imitating the human brain’s ability to read and interpret handwritten characters on a paper surface so that the computer can understand and process the data. When compared to manual processing, automated processing of handwritten material improves data processing speed and provides very high recognition accuracy at a low cost. The segmentation method makes use of information on the shape and geometry of English letters. The method of splitting images into various areas so that letters may be recovered from texts is known as segmentation. The purpose of segmentation is to make an image more relevant and easier to evaluate by simplifying or changing its representation. Different errors occurring during the segmentation are bad segmentation, over-segmentation, and miss segmentation. Because of the unique form of each individual’s handwriting, character segmentation of handwritten texts presents a number of problems. This study seeks to overcome all these issues by designing an automated segmentation algorithm to segment characters generated from handwritten text. Reading postal addresses, bank check amounts, and forms are some applications of handwritten character recognition.
