OCR Solutions and AI Techniques for Intelligent Data Extraction

Introduction

Businesses and organizations compete to stay ahead in this fast-paced environment. Data entry is the most difficult task in this fast-paced world. It is a huge responsibility and takes a lot time to enter data from hard forms into online forms. Even though a large staff of managers manually inputs data into the systems to ensure accuracy, it is not guaranteed. The human eye can make mistakes no matter what. Gartner's research states that businesses spend 3% of their revenue on paper consumption. Composed paper is 50% of businesses' waste. The unit 4 study also states that office workers spend an average of 69 days a year on administrative tasks. This results in an annual loss of $5 trillion in productivity.

OCR is the best answer for this plight. Optical Character Recognition (OCR) is a technology solution to data entry. This automated optical character recognition solution has been a huge success for many businesses. This blog will discuss how OCR and artificial intelligence make data extraction faster and more precise.

What is OCR technology?

OCR technology extracts data from an image or document and converts it into text. OCR technology is able to scan documents such as ID cards, driver's licenses, utility bills and receipts. It can also scan contracts, invoices or passports. These documents can be taken from the printed or handwritten form, and converted into machine-readable messages so that the system can read it and display it online. Workers will no longer have to spend hours typing every document online.

Traditional OCR systems work by looking for patterns in the image and then analysing it. It extracts any text from the image and converts it into a format that is usable by machines. The digital editor converts the scanned document into an editable format.

This OCR Training Dataset can only extract data and convert it to digital form. It is not yet possible to guarantee how accurate and error-free this task will be completed. This is because the labor required to correct the errors is still needed and the time wasted. Its growth has been halted by the demands of businesses. Data extraction needs to be more intelligent.

Artificial Intelligence to Save OCR

OCR is based on artificial intelligence and uses a machine learning algorithm to extract data. The OCR uses computer vision and language processing algorithms to extract text from its image form. This allows for more precise results. AI-based technology allows the OCR to understand the language, type, format, context, and other details of the document. AI-based OCR provides a complete understanding of the data contained in a document. It ensures a 99 percent accuracy rate. AI-based OCR engines eliminate the need for human assistance to edit any text.

OCR based on AI has three steps:

Preprocessing

Preprocessing images using different techniques is necessary to ensure that characters are correctly recognized.

De-Skew, Despeckle. The De-skew method is used to align the document perfectly. This method extracts data without leaving any marks or pages that are too crumbly and aligns it correctly. This also smoothens out the edges and removes any spots.

Binarisation. Binary images are grey-scale images, i.e. A binary image is a grey-scale image, i.e. a black and white picture. Binarization is when colored images are converted into binary images. This is necessary because most OCR software works with binary images. This affects the quality of recognition.

Layout Analysis and Line Removal Analysis. This is used to identify columns, paragraphs, and other details. This filter removes non-glyphs lines and boxes. This allows Dataset For Machine Learning to be extracted in a thorough manner. Data written in column format can also be identified.

Script Recognition. It is important to identify the script before you begin the data character recognition process. A script can be modified through the level words, especially when dealing with multilingual documents. This helps improve data extraction.

Character Recognition

Character recognition can be achieved in two ways: pattern recognition or feature extraction. The matrix Matching algorithm is used to recognize patterns. This allows the image to be compared with the stored glyph. For documents with the same font, pattern recognition can be used. Multilingual documents can make pattern recognition difficult. Feature extraction doesn't identify the character in its entirety, but it does identify individual components of the character by breaking it down into features.

Automated Population

This automates data entry. Verification fields are used to save time and ensure that the stored data is correctly populated. Post-processing techniques can enhance OCR engines. Near neighbor analysis is one of these techniques. It corrects errors and highlights words that should be written together.

Many businesses have found that the AI-based OCR engine has reduced their burden. Their data entry process is now smooth and can be completed quickly without having to waste time on a tedious, long process.

GTS And OCR Solutions

Global Technology Solutions (GTS) OCR has got your business covered. With its remarkable accuracy of more than 90% and fast real-time results, GTS helps businesses automate their data extraction processes. In mere seconds, the banking industry, e-commerce, digital payment services, document verification, barcode scanning, Image Data Collection, AI Training Dataset, Video Dataset along with Data Annotation Services and many more can pull out the user information from any type of document by taking advantage of OCR technology. This reduces the overhead of manual data entry and time taking tasks of data collection.

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