Optical character recognition (OCR) transforms images into an electronic form primarily by analyzing the patterns of light and dark within the image to identify and interpret characters. This process involves detecting the shapes and structures of letters and symbols as they appear in the image. Using algorithms and machine learning techniques, OCR systems can recognize these patterns and translate them into recognizable text characters.
This method allows OCR to effectively convert printed or handwritten text from images into machine-readable formats, such as plain text files or structured data. The transition from image to text relies heavily on the ability to discern individual characters based on their visual representations in various fonts and styles, making pattern recognition a fundamental aspect of how OCR works.
Other options do not fully encapsulate the core functionality of OCR. Converting images into binary code is a more basic, low-level representation that does not involve interpreting characters, while extracting metadata pertains to additional information about the image rather than its textual content. Creating a visual representation of the text does not align with the intent of OCR, which is to convert text into a format that can be edited, searched, and processed by machines.