Living in a technology-driven world, people are in need of robust and instant procedures for their ease. The researchers and the scientists are working day and night to take full advantage of technological advancement in the shape of automated tools and applications.
Every type of business tends to interact with a large audience and requires some paperwork for getting services.
Customer document processing is a necessary part of any firm’s workflows. To do so, businesses tend to adopt manual methods which are quite hectic and time-consuming.
Due to human intervention, there was a big chance of data redundancy. Therefore, automated tools and solutions are the need of the hour for businesses. The solution must be able to fetch the data accurately and autofill the information without any errors mainly when dealing with financial and client identity documents.
To address this issue and to take businesses’ needs under consideration, optical character recognition solutions are of great significance. It provides the ultimate way of extracting and populating the clients’ information automatedly.
OCR apps are considered as data entry operators but more reliable and robust. Businesses by using OCR processes can boost up their data processing methods to improve their productivity.
AI-Driven Optical Character Recognition (OCR) – It’s Working
OCR technology is used to convert the information placed over the documents into machine-coded text. OCR services are used to automate the manual procedures of data extraction and form filings.
Simple is a way of digitizing the text onto the scanned document in order to make a digital copy that can be edited or stored electronically over the cloud.
The old-fashioned character recognition app was not automated to such an extent that it could work without human intervention. For accurate results standards and documents, templates were required. Still, such solutions were not able to extract and populate data accurately, making human involvement mandatory.
OCR solution worked accurately on such documents whose templates as well as the formats were added into the system. Unfortunately, whenever such a document tends to appear whose template and format was not uploaded into the system usually becomes problematic and unable to process with accuracy.
However, the SaaS providers have precisely embedded AI into the OCR compliance in order to make it a more flexible and robust solution.
Document processing
Document De-skewing technique
Customers’ documents need to be properly aligned and there should not be any kind of folds or spots on them. To do so, de-skew is the process that aligns the documents by tilting them a few degrees to make them perfectly fit, horizontally as well as vertically.
In addition, the OCR document scanner also smoothens the edges and also removes the spots.
Binarisation
It’s the technique of transforming the colored document copies into a grey-scale format usually termed as a binary image. It’s viable as OCR technology works more accurately on binary images as compared to the colored images. It also improves the text recognition quality which results in accurate data processing.
Script Recognition
The documents which are written in different languages may vary the meaning of words, that is why before applying OCR, the scripts must be identified. This assists the OCR services to extract the information in a more enhanced way.
Character Isolation
Usually, due to the image artifacts, some of the characters are combined together which must be separated in order to achieve accurate results from OCR scanning. This method of separating the characters is termed segmentation.
Segmentation is done by placing the patches of font onto the grid. Due to the blend of white space between the characters, the vertical lines intersect the black areas of the characters.
Character Recognition
Pattern Recognition
In order to recognize the document pattern, OCR Technology uses a matrix matching technique that allows comparing the images by placing them on the glyph pixel by pixel. This process is based on the right placement of inputs on the glyph.
It’s quite effective and works seamlessly on documents that are typewritten having the same font.
Feature Extraction
Document pattern identification could be less effective over the document having different language inputs. Using this method, instead of recognizing the whole character, the character is converted into bits and pieces in the form of a feature-like line intersection of closed loops.
Furthermore, the extracted features are visualized in the form of vector representation. By doing this the whole character recognition procedure becomes more efficient.
Automated data population
Once the data has been pre-processed by undergoing various steps the information is ready to populate into the forms. The pre-processed information which was stored into the storage is used for auto-filling the customers’ verification forms. Which saves the precious time of the client.
To enhance the OCR accuracy, the data is passed through the post-processing method. It’s called near neighbor analysis. This is used to determine the errors as well as identify the words which have to be written without spaces.
Final thoughts
Living in a fast-moving world, digital business customers are in need of fast and robust procedures. The people are so busy in their life that no one has the time to wait in a long lengthy verification form.
OCR apps have become a business necessity as they can automate the manual way of data entry. Using OCR technology the business can uplift their pre-processing procedure and can boost up the customer’s experience.
