OCR – OPTICAL CHARACTER RECOGNITION

OCR – OPTICAL CHARACTER RECOGNITION – WHAT IS OCR?

The Optical Character Recognition (OCR) process aims to convert image-based text into a format that can be understood by computers. For example, when you scan a form or receipt, the computer saves the scan as an image file, and you cannot edit, search, or count the words in that image using a word processor. However, using OCR allows you to convert the image into a text document, where the content can be stored as textual data.

Why is OCR (Optical Character Recognition) Important?

Most business processes often require the collection of information from printed sources such as forms, paper invoices, legal scanned documents, and printed contracts. Managing this large volume of paperwork not only takes time but also consumes valuable storage space. While paperless document management is an efficient method, the process of scanning documents into images often encounters challenges, requiring manual intervention, and can become tedious and complex.

Moreover, digitizing these documents results in image files containing hidden text, which cannot be processed directly by word processing software as traditional text documents can. OCR technology addresses this issue by converting image-based text into textual data that can be analyzed by other business software. The data can then be used for analysis, process optimization, automating activities, and improving work efficiency.

What is the OCR Working Mechanism?

OCR tools or software follow a detailed process with the following steps:

1. Image Capture: A scanner reads the document and converts it into binary data. The OCR software analyzes the scanned image, classifying light areas as background and dark areas as text.
2. Preprocessing: The OCR software performs preprocessing by cleaning up the image and removing errors. Cleaning techniques include straightening or skewing the document, removing dot noise, and cleaning up the border frames.
3. Optical Character Recognition (OCR) for Multilingual Text Recognition: This process uses two main algorithms, template matching and feature extraction.

• Template Matching: This involves isolating a character image and comparing it with stored templates. It is most effective when used with scanned images from typed documents.
• Feature Extraction: This method breaks the character image down into features such as straight lines and curves, then searches for the best match among various character shapes.
• Post-Processing: After analysis, the system converts the text data into a file on the computer. Some OCR systems can generate PDF files with annotations, including both the pre- and post-scanned versions of the document.

Common OCR Applications

Data experts classify OCR technology based on specific purposes and applications. Here are some examples:

1. Basic Optical Character Recognition Software: A simple OCR tool stores many different text image samples and fonts. It uses pattern-matching algorithms to compare each character with the internal database. This is known as optical word recognition, but it has limitations because it cannot store all types of fonts and styles.
2. Intelligent Character Recognition Software: Modern OCR systems use Intelligent Character Recognition (ICR) technology to read text similar to how humans do. It utilizes machine learning to train the system, where neural networks analyze the text at multiple levels to detect various image attributes and produce the final output.
3. Smart Word Recognition: This system processes the entire image, rather than preprocessing it into characters like ICR. It uses methods similar to ICR to understand and process the full image of a word.
4. Optical Symbol Recognition: Optical Symbol Recognition identifies logos, icons, and text symbols in documents.

Benefits of OCR:

Here are the key benefits of Optical Character Recognition (OCR) technology:

Searchable Text

Businesses can convert existing and new documents into a fully searchable information repository. They can also process text-based databases automatically using data analysis software to extract deeper insights.

Operational Efficiency

You can improve efficiency by using OCR software to automate document workflows and digital workflows within your business. Here are some examples of what OCR software can do:

• Scan handwritten forms for verification, review, editing, and automatic analysis. This saves time compared to manual document processing and data entry.
• Quickly search for specific documents by searching a phrase in the database, so you don’t have to manually sift through filing cabinets.
• Convert handwritten notes into editable text and documents.

Artificial Intelligence Solutions

OCR is often part of other artificial intelligence solutions that businesses can implement. For example, OCR is used in self-driving cars to scan license plates and road signs, detect brand logos in social media posts, or identify product packaging in advertising images. Such AI technology helps businesses make better marketing and operational decisions, reduce costs, and enhance customer experiences.

What is OCR Used For?

Here are some common OCR use cases across various industries:

Banking

The banking industry uses OCR to process and verify paperwork for loan documents, deposit checks, and other financial transactions. This verification improves fraud prevention and enhances transaction security. For example, BlueVine, a fintech company that provides funding to small and medium-sized businesses, used Amazon Textract, a cloud-based OCR service, to develop a product that quickly helped small businesses in the U.S. access Paycheck Protection Program (PPP) loans as part of the COVID-19 relief stimulus. Amazon Textract automatically processed and analyzed tens of thousands of PPP forms daily, enabling BlueVine to help thousands of businesses receive funds, saving over 400,000 jobs.

Healthcare

The healthcare industry uses OCR to process patient records, including treatment histories, test results, hospital records, and insurance claims. OCR helps streamline workflows and reduces manual tasks in hospitals while ensuring records are always up to date. For example, nib Group, which provides health and medical insurance to over 1 million Australians, receives thousands of healthcare insurance claims every day. Customers can take pictures of their medical invoices and submit them via the nib mobile app. Amazon Textract automatically processes these images, allowing the company to approve insurance claims much faster.

Logistics

Logistics companies use OCR to track shipping labels, invoices, receipts, and other documents more efficiently. For example, Foresight Group uses Amazon Textract to automate the invoice processing in SAP. Manually entering these business documents is time-consuming and prone to errors, as Foresight’s employees had to input data across multiple accounting systems. With Amazon Textract, Foresight’s software can accurately read characters on various layouts, improving business efficiency.

How BPO.MP Can Assist with OCR?

BPO.MP offers services that can help you implement OCR in your business:

ProEye: This machine learning (ML) service uses OCR capabilities to automatically extract text, handwriting, and data from scanned documents like PDFs. The service can read thousands of documents with different layouts and formats at high speed. When extracting information from documents, ProEye returns a confidence score for each piece of content it identifies, allowing you to make informed decisions about how to use the extracted results.

ProEye can analyze millions of images and videos in minutes, enhancing human visual assessment tasks with artificial intelligence. You can use its Recognition APIs to extract text from both images and videos. This includes extracting distorted or skewed text from images and videos related to street signs, social media posts, and product packaging.

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