Document imaging is a methodology used for replicating a document. Document imaging can be done through printers, copiers, scanners, and computer output microfilms. It’s commonly used in software based systems where the images and texts are captured, stored, and reprinted through computers.
Optical Character Recognition (OCR) is a methodology used for translating scanned images to readable formats in printed, typewritten, handwritten or machine encoded text. OCR uses the techniques of character recognition, computer vision, and artificial intelligence to produce output. OCR reads a set of characters, words, and phrases of the text, matching each character to the characters in the database. Later, it matches the string of characters to form the word. Earlier OCR systems matched bitmaps of specific fonts to recognize letters, symbols, and words. The latest systems have been developed to differentiate between texts, letters, and images. Online character recognition is also known as dynamic character recognition, intelligent character recognition, and real time character recognition. The main concern of OCR is the accuracy rate which is generally affected by dynamic motion of the written text in cursive handwriting. It’s possible to achieve an accuracy of 99 percent with the method.
OCR provides features such as machine translation, text mining, and text-to-speech which are used in advanced scanning applications. Previously, it’s used by various government organizations to get e-documents quickly. Now, it’s commonly used by enterprises to recognize a complex problem which is done via intelligent character recognition. OCR uses artificial neural networks to analyze written texts clearly and it’s also used in video mode in the iPhone.
Optical Character Recognition