Increase Your Business' Operational Ability with OCR Services

Today, many companies manually extract data from scanned documents and thus consider documents processing to involve the most manual work out of all back-office functions.

Thanks to the OCR software, as it eliminates manual and expensive processes by minimizing the costs related to the human factor. OCR software automates work in companies and public institutions providing quick access to key data from various sources including graphic documents, PDFs, paper documents, e-mails, faxes and more. An OCR software, when tailor-made, can be trained or configured to automatically read and process any type of document, while accurately extracting text, handwriting, tables and other data without any manual effort.

An enterprise grade system like Azure Form Recognizer can be leveraged to automate your data processing, enhance your data-driven strategies, and enrich document search capabilities.

However, a custom-built OCR solution based on your business needs, applications and workflows enables you to quickly automate document processing and act on the information extracted. For example, to automate loans processing or to extract information from invoices and receipts.

Using OCR technology, an application extracts and analyzes form fields, text, and tables from your documents and converts them into a machine-readable format. Smart document processing solutions including OCR software uses machine learning models to analyze your forms and documents, extract text and data, map field relationships as key-value pairs, and returns a structured JSON output.

There are quite many solutions available in the OCR software market that leverage OCR technology to provide automated document processing solutions that enable you to quickly get accurate results that are tailored to your specific content without excessive manual intervention or extensive data science expertise.

Form Recognizer can also be used to automate your data processing in applications and workflows, enhance data-driven strategies, and enrich document search capabilities.

Form Recognizer easily identifies, extracts, and analyzes document data including:

• Table structure and content
• Form elements and field values.
• Typed and handwritten alphanumeric text.
• Relationships between elements.
• Key/value pairs
• Element location with bounding box coordinates.

Business Solutions like Microsoft 365 tools and Azure Form Recognizer are a few of the commonly used technology solutions that leverage AI and machine learning models to extract and analyze form fields, text, and tables from your documents.

Azure Form Recognizer analyzes your forms and documents, extracts text and data, maps field relationships as key-value pairs, and returns a structured JSON output. You quickly get accurate results that are tailored to your specific content without excessive manual intervention or extensive data science expertise.

Similarly, Amazon Textract, a machine learning service that automatically extracts text, handwriting and data from scanned documents that goes beyond simple optical character recognition (OCR) to identify and extract data from forms and tables.

Textract uses machine learning to read and process any type of document, accurately extracting text, handwriting, tables and other data without any manual effort.

(We are using cloud-based services and hosting) An enterprise grade OCR solution can extract the data in minutes vs. hours or days in case of manual typing. A fast secretary types some 200 characters per minute whereas the fastest OCR software recognizes some 1,600 characters per second.

For any business, the accuracy of the converted data is important. However, the accuracy of an OCR software depends on how often a character is recognized correctly versus how often a character is recognized incorrectly. Most OCR software offer 98 to 99 percent accuracy. When the accuracy is measured at the page level 98-99% accuracy would mean that 980 to 990 characters will be accurate in a page of 1,000 characters. It is often fairly acceptable in most cases and can be combined with human feedback for better results.

Although many OCR solutions claim about it but no data extraction model is 100% accurate. Hence, in certain cases, it is important to use a supervised learning model to improve the accuracy of your document processing workflow. Many solutions out there encompass a layer of human intervention often called as a human-in-the-loop that reviews any document that is flagged in red. Higher the number of documents that are processed and reviewed, higher is the accuracy level of the data extraction model.