...bank deposit slips, reading postal addresses, extracting information from cheques, data entry, applications for credit cards, health insurance, loans, tax forms etc. are application areas of digital document processing. This paper gives an overview of research work carried out for recognition of hand written English letters. In Hand written text there is no constraint on the writing style. Hand written letters are difficult to recognize due to diverse human handwriting style, variation in angle, size and shape of letters. Various approaches of hand written character recognition are discussed here along with their performance. Fig 1.Major Steps of an OCR System Index Terms— Offline Hand written Character Recognition, Pre-Processing, Feature Extraction, Classification, Post Processing. I. INTRODUCTION Optical Character Recognition (OCR) is one of the most fascinating and challenging areas of pattern recognition with various practical applications. It can contribute immensely to the advancement of an automation process and can improve the interface between man and machine. It is the mechanism to convert machine printed, hand printed or hand written document file into editable text format. Typically, there are two different categories of handwriting character recognition: off-line and...
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...EEN 538: DIGITAL IMAGE PROCESSING Optical Character Recognition (OCR) using binary image processing with MATLAB Abstract- Nowadays, Optical Recognition is becoming a very important tool in several fields: medicine, physics, cosmology, traffic (plate numbers), etc. We can also use this to recognize character for example to digitalize a book. We will talk about this last topic in this report: Optical Character Recognition (OCR). I. INTRODUCTION Once we have the b&w image we can start the segmentation process. To do that we can use the function “bwconncomp”. This function returns us a struct from where we can obtain the characters because it gives us all the connected components. Thus, we can use it to get all the character even if they have 2 or 3 objects. This function returns us the pixels of the connected components (characters) but we have to figure out from those, the coordinates of the character in the original matrix (row and columns). To do this, we will obtain the centroid of every connected component and from it and using the first and last pixel detected of the connect component, we can figure out the exact coordinates of the image. The idea is as follows: Firstly, we can to convert the number that the function returns us to a column and a row. We can do this using the total rows of the original image. Once we have the first and last pixel detected of the connect component in (row, column) we can figure out directly the x-coordinates of the character in the image...
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