In the present era, there is mass digitization of businesses and financial institutions. The fast-paced operations of banks, insurance companies, and investment firms strive to implement the best methods for providing a secure and convenient customer experience. This also helps businesses get accessed by a wider range of areas globally.
As part of the technological advancements, OCR solutions help businesses and other financial institutions save their time in processes like data extraction and data entry. Statistics forecast growth in the OCR market size to $12.6 billion by 2025.
With the technology boom, there is increasing competition between digital business platforms for the best use of the latest solutions for improving efficiency. Data entry and documentation processes used to take long hours and required hiring trained personnel.
However, OCR solutions have now made this an automated process, with data extraction being done using AI. Machines obviously process data more accurately compared to humans and reduce errors significantly while analyzing and comparing information.
OCR services eliminate the use of scanners and hardware devices for the purpose of data extraction from documents, allowing mobile OCR applications to do these tasks in much less time and with less effort.
OCR solutions from different service providers can differ considering how they are meant to be used, but the core concept is always the same. Nowadays, OCR apps make use of AI to scan, extract and process information from documents. These functionalities also include the conversion to pdf and rich text formats.
Also, character recognition apps allow much faster processing and often include filters for making the scanned images even clearer than the original hard copies. In the back-end, OCR solutions work by separating the white spaces between characters and recognizing characters separately. The characters detected as groups are formed as words.
Specified metadata is assigned to every character and then matched with previously saved fonts. In the cases where OCR solutions are unable to detect handwritten documents, there is a new technology under development. ICR (intelligent character recognition) is able to detect cursive handwriting using advanced algorithms.
With an intelligent OCR process, identifying different similar characters such as “1” and “I” is easier. The AI-based OCR solutions do this by detecting nearby characters to make decisions and recognize words.
OCR alone is quite effective in extracting and detecting characters, but the use of artificial intelligence provides much more accuracy. The combined use of AI and NLP (natural language processing) helps OCR solutions in document verification.
OCR document scanners are implemented in businesses to save working costs and the extra hardware use is reduced. Processes like data entry do not need the hiring of professionals anymore, because AI goes through a constant learning process and ‘knows’ which information needs to be extracted and where it needs to be stored.
The pre-processing stage in data extraction with OCR solutions involves functions like adjustment of brightness, contrast, and clarity of the scanned image. These functions are useful in making the text in the document more readable as the distortion is reduced.
Once the image is made clear, OCR solutions then distinguish between the different characters and identify text blocks, lines and paragraphs.
In the post-processing stage, machine learning algorithms in AI allow the smart detection of varying font styles, sizes and determine the template on which the document is based.
With OCR technology, it’s possible to perform data extraction on various types of documents, which include:
These are the documents created using pre-defined templates. Structured documents have very few flaws in formatting and spacing, like in government-issued ID documents, bills, and credit card receipts. OCR solutions provide easy data extraction from structured documents since the AI-based system is designed with defined templates.
Semi-structured documents share some characteristics of structured documents like the information is clear and easy to extract. However, these documents are not based on set templates, for example, supermarket invoices as well as purchase orders.
The documents that neither follow a defined template nor are easily readable are called unstructured documents. The difference between semi-structured and unstructured documents is the standardization level.
Unstructured documents include legal agreements including variations in the placement of dates and other important information. In any case, OCR solutions can extract data even from unstructured documents and help in making the data entry process efficient.
To summarize, OCR solutions are an essential part of the technological revolution brought together with artificial intelligence. Ongoing enhancements in technology are providing more tools to businesses for efficiency and accuracy. Similarly, OCR solutions have contributed to automating the document validation process.
