Imagine you want to edit a printed document like a book, a magazine article or a printed contract. You need to spend hours to type the document from the beginning and be careful about the mistakes. Or you can use an Optical Character Recognition (OCR) tool to scan the printed document and digitize the whole text.
OCR is a great solution for converting human-to-human communication but falls short when converting more structured documents such as forms that need to be processed by machines.
Human-to-human communication is mostly in the form of free text like the one you are reading now. Such documents are called unstructured data and while they are great for human-to-human communication but they are hard for machines to understand. OCR converts the text in unstructured data into machine readable text so it can be searched and therefore more easily consumed by humans.
To make text easier for machines to understand, companies and governments developed a myriad of forms that structure text into easily recognizable labels. OCR solutions can convert those into machine readable text but that is just the first step. Machines can not act on most text as they do not understand its meaning. Modern deep learning based data capture solutions further process OCR output, converting it into key-value pairs and tables that can be acted on by machines.
What is OCR?
OCR is a specialized technology to perceive the characters of a text within the images like printed books, photos, or scanned documents. It converts text containing images into characters that can be readable by computers to edit, compute, and analyze in the future steps. Below, you can see an example of how OCR digitalizes the text in a receipt.
Popular use cases for OCR technology include digitizing books and other unstructured documents that enable human-human communication. For example, Google translate’s OCR enables users to read in any language:
Why is OCR is no longer implemented stand-alone today in human-machine communication?
While OCR captures text and converts it machine-readable, it only provides unstructured characters. However, forms are designed for human-machine communication so machines can automatically act on the data they receive from humans. Thus, vendors need to process OCR results with machine learning to turn machine-readable data into machine-actionable data.
As explained, OCR is still a foundational technology and its performance is important. These are some of the impediments to its performance:
- The image can be skewed or non-oriented. In these cases, OCR might not recognize the characters because the text isn’t aligned. Thus, OCR software should be able to straighten and de-skew images.
- Colored and varying background patterns might be problematic as they can be reduce text recognition. Fixed backgrounds can improve OCR performance.
- Text in glared or blurry images are hard to read for humans as well machines. Higher image quality leads to higher quality OCR output.
What is data extraction?
Data extraction is the process of turning unstructured or semi-structured data (e.g. forms) into structured data (e.g. text documents, emails).
As OCR only recognizes characters from sources, data extraction does more than that. With OCR, companies get characters that have no meaning to machines. However, data extraction includes structuring this data to make it actionable. For example, data extraction automates invoice processing so payments and record keeping can be automated.
To read more about data extraction and understand why it is relevant in more detail, you can take a look at our in-depth guide to be published soon.
OCR is still a foundational technology as today’s AI vendors rely on it to extract data. While choosing an OCR vendor, you should consider the following factors:
- Character recognition accuracy
- User-friendly interface
- Computation speed
- Output file formats (Word, Excel, PDF, etc.)
- Integration with ERP data
- Learning over time
Below you can find a list of OCR vendors including relevant information which are collected from different resources. You can filter the list by focus areas, customers, and solution types.
Company Number of Employees on Linkedin Area of Focus Vital Customers Type of Solution
ABBYY FineReader 1001-5000 Document recognition, data capture, language processing Dell, Fujitsu, HP, Siemens Continuously trained ML
Datamolino 11-50 Bookkeeping automation Deloitte Not template based
Docparser 2-10 Document data extraction SMEs Template based
DocuPhase 11-50 Data capture, bookkeeping automation Lockheed Martin, Sharp Template based
Grooper 51-200 Document recognition, data capture, language processing Continuously trained ML
Hypatos 11-50 Document data extraction, advanced processing PwC, Deloitte, EY, Schwarz Gruppe Continuously trained ML
Infrrd 201-500 Document data extraction Nokia, GE Continuously trained ML
Instabase 11-50 Document data extraction Continuously trained ML
Klippa 11-50 Document recognition, data capture Endemol Continuously trained ML
Kofax 1001-5000 Document data extraction Readdy Continuously trained ML
Laserfiche 201-500 Document data extraction, document management Continuously trained ML
PDFelement 501-1000 Document data extraction Hitachi, Deloitte Template based
Rossum 11-50 Document data extraction Bloomberg, IBM, Nvidia Continuously trained ML
Scanbot 11-50 Document recognition and data capture Template based
Veryfi 11-50 Document data extraction, bookkeeping automation Continuously trained ML
A popular use case of OCR is invoice capture. With the combination of OCR and other AI techniques, companies can easily extract data from invoices. In the invoice capturing process, OCR is used to transfer data from printed invoices to digital systems. As a result, invoices can be automatically processed faster. To read more about invoice capture, you can read our article about it.