Abstract
Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research work conducted for recognition of handwritten English alphabets. In Handwritten manuscript there is no restriction on the writing technique. Handwritten alphabets are complicated to recognize because of miscellaneous human handwriting technique, difference in size and shape of letters, angle. A variety of recognition methodologies for handwritten English alphabets are conferred here alongside with their performance.
Highlights
The Optical Character Recognition (OCR) is a broad domain of research in Soft Computing, artificial intelligence (AI), pattern recognition (PR) and computer vision
The primary significant way in some recognition of handwritten English alphabets scheme is pre processing succeeded by segmentation procedure and feature extraction procedure as represents in figure: 2. The Pre processing includes the phases that are necessary to construct the raw image in a figure which is appropriate for segmentation [9]
Handwritten character identification method of lowercase English alphabets is stated in research work of [24] via employing binarized pixels of the image as features and multi layer back propagation neural network as classifier
Summary
The Optical Character Recognition (OCR) is a broad domain of research in Soft Computing, artificial intelligence (AI), pattern recognition (PR) and computer vision. Dynamic knowledge, which is usually available for online text recognitions, is total strokes, set of strokes, approach of stroke, and velocity of writing within each stroke This precious information helps in recognition of articles and often guides to enhance performing methods contrasted to offline. Due to the variation of styles in handwriting and un-benchmarked nature of handwritings, the problem of offline handwriting recognition is the main exigent complexity in OCR and it usually requires language specific techniques. On the another hand, OCR of typed articles are extremely much in demand for practical applications such as historical article analysis, official note and article processing, and vehicle plate recognition.
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