What is optical character recognition (OCR)?

The OCR engine or OCR software works through the following steps:

A scanner reads the documents and converts them into binary data. OCR software analyzes the scanned image and classifies light areas as background and dark areas as text.

The OCR software first cleans the image and removes errors to prepare it for reading. Here are some of the cleaning techniques:

• Slightly straightens or skews the scanned document to fix alignment issues during scanning.
• They remove or remove blemishes from digital images or soften the edges of text images.
• The boxes and lines in the image are cleaned.
• Hyphens are recognized for multilingual OCR technology.

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.

Pattern matching isolates a character image, called a glyph, and compares it to a similarly stored glyph. Pattern matching only works if the stored glyph has a similar font and scale as the input glyph. This method works well with scanned images of documents that have been written in a known font.

Feature extraction splits or decomposes glyphs into features such as lines, loops, line direction, and line intersections. It then uses these features to find the best match or nearest neighbor among the stored glyphs.

After analysis, the system converts the extracted text data into a computerized file. Some OCR systems can create annotated PDF files that include the versions and later versions of the scanned document.

Data scientists classify the different types of OCR technologies based on their uses and applications. Here are some examples:

A simple OCR engine stores many text image patterns and many different fonts as templates. OCR software uses pattern – matching algorithms to compare images of text, character by character, with its internal database. If the system matches the text word for word, it is called optical word recognition. This solution has limitations as there are virtually unlimited fonts and writing styles and not every font can be captured and stored in the database.

Modern OCR systems use Intelligent Character Recognition (ICR) technology to read text the same way humans do. They use advanced methods that train machines to behave like humans using machine learning software . A machine learning system called a neural network analyzes the text at many levels and processes the image iteratively. It looks for different image attributes, such as curves, lines, intersections, and circuits, and combines the results of all these different levels of analysis to get the final result. Although ICR typically processes images one character at a time, the process is fast and results are available in seconds.

Intelligent word recognition systems work on the same principles as ICR, but process whole word images instead of pre-processing the images into characters.

Optical mark recognition identifies logos, watermarks, and other text symbols on a document.

Here are the main benefits of OCR technology:

Businesses can turn their existing and new documents into a fully searchable knowledge archive. They can also process the text database automatically by using data analysis software for further knowledge processing.

You can improve efficiency with OCR software to automatically integrate document workflows and digital workflows within your business. Here are some examples of what OCR software can do:

• Scan hand-filled forms for automated verification, review, editing, and analysis. This saves the time required for manual document processing and data entry.
• It finds the required documents by quickly searching for a term in the database so you don’t have to manually sort files into a box.
• Turn handwritten notes into editable text and documents.

OCR is often part of other artificial intelligence solutions that companies can implement. For example, it scans and reads license plates and road signs on autonomous vehicles, detects brand logos in social media posts, or identifies product packaging in advertising images. This artificial intelligence technology helps companies make better operational and marketing decisions that reduce expenses and improve the customer experience.

The banking industry uses OCR to process and verify paperwork for loan documents, deposit checks, and other financial transactions. This verification improved fraud prevention and enhanced transaction security. For example, BlueVine is a financial technology company that provides financing to small and medium-sized businesses. Used Amazon Textract, a cloud-based OCR service, to develop a product for small businesses in the US to quickly access Paycheck Protection Program (PPP) loans as part of the US stimulus package. COVID-19 help. Amazon Textract automatically processed and analyzed tens of thousands of PPP forms per day so that BlueVine could help thousands of businesses obtain funding, saving more than 400,000 jobs in the process.

The healthcare industry uses OCR to process patient records, including treatments, tests, hospital records, and insurance payments. OCR helps streamline workflow and reduce manual work in hospitals while keeping records up to date. For example, nib Group offers health and medical insurance to more than a million Australians and receives thousands of medical requests every day. Customers can take photos of their medical bills and submit them through the nib mobile app. Amazon Textract processes these images automatically so the company can approve claims much faster.

Logistics companies use OCR to track package labels, invoices, receipts, and other documents more efficiently. For example, Foresight Group uses Amazon Textract to automate invoice processing in SAP. Manual entry of these business documents was time-consuming and error-prone, as Foresight employees had to enter the data into multiple accounting systems. With Amazon Textract, Foresight software can more accurately read characters in many different layouts, increasing business efficiency.