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Ocr Python: What You Should Know

Optical characters are those which can be read from one way and viewed or printed in two ways. Optical characters are those which are easy to recognize on a computer screen, as they have similar characteristics to the human eye which are easily recognizable. Optical characters are usually written in Latin letters and Roman numbers. This tutorial is an introduction to learning Optical Character Recognition (OCR) with Python. Basic Concepts — Basic concepts used in OCR are described by Wikipedia. Optical Character Recognition (OCR) can be defined as “a method of automatically recognizing objects in an image without the need of a second camera” [1]. It consists of a set of rules for determining what an object is based on its shape, and a mathematical model for detecting that object [2]. These two can be seen as the two parts of the OCR process. The algorithm uses an algorithm (or set of rules and rules of inference) to find an identifiable image. The data is then fed into a recognition engine (OpenCV or Tesseract) which is basically a pre-trained computer. OCR takes the extracted image (or data) and converts it to an editable (usually readable) language. OCR is used to read large amounts of information from a series of images (such as scanned documents) and convert the image representation into a readable representation of the information found in the image. With OCR you can also create new images. OCR can be compared to a machine-vision process that takes an image and applies an algorithm to figure out the details of that image, even though the number of dimensions may be so large that conventional algorithms cannot handle it. In my experience, OCR is much faster than regular machine-recognition algorithms. It does not require a second camera. OCR is especially suited for digitizing handwritten documents, such as legal documents.

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