The analysis of these characters is not done in bulk but in small segments at a time. The dark areas, or characters, identify numbers and letters. Once the backgrounds and characters have been separated, it is possible to initiate the process of identifying the specific contents of the characters. That process is otherwise referred to as converting the image into backgrounds (white, blank areas) and characters (evidently dark areas). In this case, the areas of interest are the ones that contain text, considering the empty sections as null. During this process, it’s important for the OCR technology to define the areas of interest in the image. For example, if there’s a document on a piece of paper, the hardware component achieves rendering a digital copy of that exact same document. To begin, the hardware component, which can be any type of optical scanner, converts the document's physical shape into a digital image. Let’s take a closer look at how the OCR system works for the former process. PDF to text file conversion, on the other hand, eliminates the need for the additional scanner component. There must be the presence of a device that scans the physical document and then the software, which converts it into digital code. Optical character recognition is primarily achieved with the combination of both hardware and software elements. Despite its difficulty and limitations, Handwriting OCR technology nonetheless exists but requires persistent training for optimal pattern recognition. It’s often even difficult to read other people’s handwriting with our own two eyes, let alone expect AI to annotate each word with maximal accuracy. Letters and words can significantly differ in style than typed text, impacting readability. Written handwriting, on the other hand, is unique to each individual, making it that much harder for even AI to encode. Traditional OCR considers the extraction of text from common font styles that the OCR system can easily be trained with. In essence, both aim to reach the same result but differ in the information they encode. Typically, OCR is divided into two types - traditional OCR and Handwriting OCR. From here, the information obtained from OCR may be applied to a vast array of uses that range from personal use to public security. With OCR, we can encode printed text from an image, allowing it to be electronically altered, searched, stored more compactly, presented on the web, and utilized in machine processes like cognitive computing and more. It is a field of study in artificial intelligence, more specifically tied to computer vision and pattern recognition. When we say Optical Character Recognition, we refer to the process of extraction and conversion of handwritten or typed text from image, video, or scanned documents like PDF to a digitally modifiable format. What is Optical Character Recognition (OCR)?
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