![]() There are numerous applications of OCR any business managing physical paperwork stands to benefit from its usage. Restrict the AI’s output to only those words/format to ensure no interpretations fall outside of the lexicon. One method is to train the AI on a specific lexicon of words that will be found in the document. In step three, AI corrects errors in the resulting file. An “H” for example has two vertical lines and one horizontal in between the machine will use those feature identifiers to identify all “H”s on the envelope.Īfter the machine has identified the characters, they’re converted to an ASCII code that can be used for further manipulations. Features may include the number of angled, crossed, or horizontal lines and curves in a character. Feature extraction: To recognize new characters, the algorithm applies rules regarding specific character features.The algorithm compares the characters on the scanned envelope image to the characters it has already learned in order to identify matches. Pattern recognition: Teams train the AI algorithm on a variety of text, text formats, and handwriting.Typically, AI targets one character, word, or block of text at a time using one of the following methods: The OCR system may also categorize the image into separate elements if needed, such as tables, text, or inset imagery.ĪI analyzes the dark areas of the image to identify letters and numbers. The resulting image is converted to a black and white version, which is then analyzed for light areas (background) versus dark areas (characters). The goal of this step is for the machine to be accurate in its rendition, but also to remove any unwanted distortions. ![]() In step one, the hardware (usually an optical scanner) processes the physical form of the document into an image – such as an image of an envelope. Think of this in context of postal and mail sorting services – OCR is core to their ability to operate quickly in processing destination and return addresses to sort mail faster and more effectively. The system’s goal is to scan the text of a physical document and translate the characters within that document to a code that’s then used for data processing. Researchers expect demand in AI-powered OCR to continue as these tools become more efficient and cost-effective.Īn OCR system features a combination of hardware and software. Reducing tedious administrative work can be critical to maximizing employee engagement and reducing turnover. This document comprehension capability helps businesses analyze numerous documents without committing human labor to the task. With AI tools, however, an algorithm can review the entire document, calculate that the subtotals for services provided should add up to $5,000, and fix the error without a human needing to supervise. Before AI, the OCR tool wouldn’t pick up on this mistake and it would be up to human review to catch it. Let’s say the scanner identified the invoice total as $500, when it was really $5,000. Handwriting still presents a challenge to AI due to the uniqueness of each individual, but with more handwriting training data, machines are gaining greater ability on that front as well.Īs an example of AI-powered OCR, imagine an OCR tool was converting print invoices into digital copies. AI can better interpret handwriting as well, opening up opportunities for digitizing a wider range of documents. Naturally, this eliminates the need for physical storage space, a cost-savings for businesses that heavily rely on documentation, such as mortgage brokers or legal firms.Īs teams combine OCR with AI and machine learning (ML) techniques, they’re able to use machines to more accurately convert text and check for errors that may occur during the conversion. They can also send it easily via email, include it in a website, and store it in compressed files. Once OCR converts a hard copy into its digital form, viewers can edit, format, and search the document. With OCR, the conversion happens quickly and with greater fidelity to the original content. Before the invention of OCR, converting physical text to digital was a manual effort: a person would have to retype each document, a time-consuming task prone to mistakes. ![]()
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