What is Optical Character Recognition (OCR)

Optical Character Recognition (OCR) is the process that converts an image of text into a machine-readable text format. For example, if you scan a form or a receipt, your computer saves the scan as an image file. You cannot use a text editor to edit, search, or count the words in the image file. However, you can use OCR to convert the image into a text document with its contents stored as text data.

What are the benefits of OCR?

The following are major benefits of OCR technology:

Searchable text

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

Operational efficiency

You can improve efficiency by using 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, reviews, editing, and analysis. This saves the time required for manual document processing and data entry.
• Find the required documents by quickly searching for a term in the database so that you don’t have to manually sort through files in a box.
• Convert handwritten notes to editable texts and documents.
   

Artificial intelligence solutions

OCR is often part of other artificial intelligence solutions that businesses might implement. For example, it scans and reads number plates and road signs in self-driving cars, detects brand logos in social media posts, or identifies product packaging in advertising images. Such artificial intelligence technology helps businesses make better marketing and operational decisions that reduce expenses and improve the customer experience.

What are the different OCR use cases in document processing?

OCR is an integral part of enterprise document processing workflows. Consider the following use cases.

Intelligent search of document archives

OCR technology enables the creation of searchable digital archives by extracting text from image-based and PDF documents. Once the text is recognized, it can be indexed and used in AI-powered search systems. Users can search for relevant files across large file volumes quickly and accurately, without additional document classification. For example, searching for a specific customer name would return all pay orders, invoices, and forms that were originally submitted as paperwork.

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

Natural language processing

OCR recognizes and extracts text at the word, line, or table-cell level, offering greater control over how content is prepared for downstream natural language processing (NLP) tasks like document classification, summarization, sentiment analysis, topic modeling, entity recognition, and more. For example, summarization will require text extraction in paragraphs, but entity recognition may prefer text extraction in key-value pairs, like a JSON file.

Data standardization

Document workflows often involve unstructured data from different formats and industries. OCR helps normalize this data by extracting both text and tables from diverse document types like financial statements, clinical notes, and technical reports. You get faster processing and more consistent data handling across systems.

Automating form processing

OCR technology plays a key role in automating form processing. It can identify fields and extract structured information from various form types, allowing businesses to integrate this data directly into databases without manual entry.

Application feature

OCR capabilities can be embedded directly into business applications so users can perform real-time text extraction themselves. This reduces analytics workload as data is collected properly at the source.

How is OCR used in different industries?

The following are some common OCR use cases in various industries:

Banking

The banking industry uses OCR to process and verify paperwork for loan documents, deposit checks, and other financial transactions. This verification has improved fraud prevention and enhanced transaction security. For example, BlueVine is a financial technology company that provides financing to small and medium-sized businesses. It 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 COVID-19 relief stimulus package. Amazon Textract automatically processed and analyzed tens of thousands of PPP forms per day so that BlueVine could help several thousand businesses get funds, saving over 400,000 jobs in the process.

Healthcare

The healthcare industry uses OCR to process patient records, including treatments, tests, hospital records, and insurance payments. OCR helps to streamline workflow and reduce manual work at hospitals while keeping records up to date. For example, the nib Group provides health and medical insurance to over 1 million Australians and receives thousands of medical claims per day. Its customers can take photos of their medical invoice and submit them through the nib mobile app. Amazon Textract processes these images automatically so that the company can approve claims much faster.

Logistics

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

How does OCR work?

The OCR engine or OCR software works by using the following steps:

Image acquisition

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

Preprocessing

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

• Deskewing or tilting the scanned document slightly to fix alignment issues during the scan.
• Despeckling or removing any digital image spots, or smoothing the edges of text images.
• Cleaning up boxes and lines in the image.
• Script recognition for multi-language OCR technology

Text recognition

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

Pattern matching

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

Feature extraction

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

Postprocessing

After analysis, the system converts the extracted text data into machine-readable text documents. Some OCR systems can create annotated PDF files that include both the before and after versions of the scanned document.

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Reference and read more:

https://aws.amazon.com/what-is/ocr/#what-is-ocr-optical-character-recognition–15s0dvp