Get Understanding Optical Character

There are two main methods for extracting features in OCR: In the first method, the algorithm for feature detection defines a character by evaluating its lines and strokes. In the second method, pattern recognition works by identifying the entire character.

OCR stands for "Optical Character Recognition." It is a technology that recognizes text within a digital image. It is commonly used to recognize text in scanned documents and images. OCR software can be used to convert a physical paper document, or an image into an accessible electronic version with text.

OCR accuracy can be measured by the portion of characters in a text that the OCR tool can extract without mistakes. For example, 99% accuracy means that 990 out of 1000 characters are correctly recognized.

OCR (optical character recognition) is the use of technology to distinguish printed or handwritten text characters inside digital images of physical documents, such as a scanned paper document.

Accuracy: Algorithms like CRNN and CRNN + CTC offer high accuracy due to their contextual understanding and sequence handling. Tesseract OCR, while accurate, might need help with handwritten or complex layouts.

It consists of a pre-processing stage where documents are converted into binary images, a top – down segmentation technique that extracts the characters, the creation of a database by the extracted characters and a recognition stage where the database is used for converting any document into text file.

In short, optical character recognition software helps convert images or physical documents into a searchable form. Examples of OCR are text extraction tools, PDF to . txt converters, and Google's image search function.

The two main types of OCR algorithms or software processes that an OCR software uses for text recognition are called pattern matching and feature extraction.