OCR Solutions – Extracting Information From Documents Made Easier

OCR Solutions - Extracting Information From Documents Made Easier

OCR solutions provide an easy way to extract the desired information from any printed hard copy or written paper. Optical character recognition is a technology in which data is read from paper using algorithms and then converted into digital format.

OCR solutions have been incorporated for decades, and this technology, like all others, faces constant improvements to the algorithms in the back end. For this reason, the OCR applications today are implemented by major businesses and have become a reliable security measure.

According to research, the market value of OCR solutions will rise up to 12.6 billion dollars by 2025.

What is OCR?

These are modern times where everything is becoming easier and faster thanks to advancements in technology. This is the same for processes like data entry and documentation, etc. Humans had the job to read data from documents and then enter it in the specific fields in the computer. But now OCR Applications have made everything easier. Optical Character Recognition technology automates the whole process of extracting data/information from documents.

Typically we are not able to edit PDF files. However, with OCR scanner apps it has been made possible. Optical character recognition technology allows you to search the entire text for specific words, as well as highlighting, copying and rewriting sections of the text.

Similarly, OCR document scanners have replaced the typical scanners and made the process much faster. Within a matter of seconds, a document in hard form can be converted into a digital text file.

How OCR Solutions Work

OCR services work slightly differently from each other depending on the way they are built, but they all have some common principles. Every OCR company offers its services but obviously, there are slight variations that make them different from each other.

OCR solutions involve a document processing system that makes the text in the document clear and easier to read. The white parts of the page are separated from the obvious characters. Then the characters are recognized as words and grouped words become sentences. The tricky part in the process is to distinguish different characters and words and then assigning the correct metadata to them. The characters are cross-checked with common fonts in the available libraries in the back end, and the suitable character is assigned. Advanced techniques are used for the characters that remain unrecognized.

When the OCR application finds a problem differentiating between the characters “1” and “I”, it searches for the surrounding characters to determine which one of them makes better sense.

AI-Based OCR 

AI-based OCR solutions implement different technology from normal OCR, integrating machine learning, computer vision, and natural language processing (NLP) algorithms to give better results. AI-based OCR solutions are very efficient and they eliminate the need for humans to check and make corrections in the scanned documents.

AI-based OCR solutions work in the following sequence of steps:

  • Pre-Processing

This is the phase where the brightness, contrast, and distortion in the image are adjusted

  • Data Extraction

The text is difficult to detect, so the software attempts to find text-block structures and separations in lines/paragraphs

  • Post-processing

The machine learning process extracts and differentiates between different font styles, sizes, and different types of documents

OCR allows extraction of data from structured, semi-structured documents and unstructured documents. Some examples of structured documents are ID cards, driving licenses, utility bills, and credit cards, as all of these have a predefined structure that can be easily detected.

Semi-structured documents include those in which the position of the information fields can vary. The format, however, is easy to read. Examples of semi-structured documents are invoices, emails, sales, and purchase orders.

The difference between semi-structured and unstructured documents is the standardization level and the information density. A defined format is not followed in an unstructured document. For example, there are always dates and definitions in legal agreements, but they vary from one another depending on the people and the negotiation. OCR solutions extract data even from unstructured documents seamlessly.

OCR for Multilingual Documents

Differences in formats and data placement were one thing, but now OCR Technology has become so advanced that it is offered for multilingual documents. Every country has its own documents and they are in different languages, with the same information written in English as well.

AI-based character recognition apps are capable of detecting two different languages at the same time. They use saved dictionaries of the respective languages and other relevant data to get high-quality extraction of data.

Conclusion

To wrap it up, Optical Character Recognition, among other technologies, has really helped speed up the data entry/analysis processes in organizations. Various OCR service providers offer their services and companies benefit from them.

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By Charlotte Russe

Content Writer for AMIR ARTICLES – HOME OF QUALITY CONTENT FOR EVERYONE - Read about Games, Tech, Business, Health, Find a Job, Blogging and Articles in Different Categories Available on Amir Articles

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