Introduction
Companies and organizations in today's fast-paced business world are always trying to be ahead. Data entry operations are the most frustrating task in this race to the top. While it is a large responsibility, data entry operations can take a lot of time. Even though large numbers of management staff manually enter the data into the system, there is no guarantee that the data will be accurate. The human eye is susceptible to making mistakes, no matter how much you do.
Gartner reports that 3% of revenue is spent by businesses on paper. 50% of the waste from composed paper is their waste. A unit 4 study found that office workers spend 69 days per year performing administrative tasks. This leads to a $5 trillion annual productivity loss.
OCR (Optical Character Recognition) is the final solution to this plight. Optical Character Recognition can be used to automate data entry. Many businesses have benefitted from this automated optical characters recognition solution. In this blog, we will talk about how OCR combined with artificial intelligence makes data extraction more precise and seamless.
What is OCR technology exactly?
OCR technology extracts data and transforms it into text. OCR technology can scan ID cards, driver’s licenses and utility bills. These documents can then be removed from their printed or handwritten forms and made machine-readable so that the system understands it and displays it in an online form. The workers won't have to spend hours typing each document online.
An older OCR system detects patterns and analyzes the image. If the image contains text, it will extract that text and create a format that can easily be read by a computer. The scanned document is converted into a digital, editable format.
OCR Training Dataset does not allow for the extraction of data and conversion to digital. This task cannot be guaranteed to be accurate or error-free. It is not an easy task as labour is still required to correct errors. Time is also wasted. Businesses have increased their demands. A more intelligent approach to data extraction is therefore essential.
Artificial Intelligence for OCR Rescue
OCR solutions are built on artificial intelligence. They use a machine-learning algorithm to extract data. It employs a combination of computer vision and language processing to extract text from images and give more precise results to the user. AI-based technology allows OCR to recognize the type of document, context, format, language and other details. AI-based OCR can provide a detailed understanding and interpretation of data in a document. It has a 99% accuracy rate. AI-based OCR engine eliminates the need to have human help in making edits.
AI-based OCR is a three-step process:
Preprocessing
The preprocessing of images is used to recognize characters in full.
De-Skew or Despeckle. De-skew is used to give the document an exact alignment. This technique extracts the data precisely without any spots or broken pages and aligns the data correctly. This will smoothen the edges of your document and also remove any spots.
Binarisation. A binary image is a greyscale image, i.e. Black and white images. Binarisation is the process of turning colored images into a binary picture. This process is essential because OCR software primarily works on binary images. This can also impact the quality and accuracy of recognition.
Layout and Line Removing Analysis. This technique can be used to identify the columns and paragraphs. It removes all non-glyphs lines, boxes, and boxes. This makes Dataset For Machine Learning extraction easier as it allows you to identify data in columns.
Script Recognition. Script Recognition. This allows for better data extraction.
Character Recognition
Two methods of character recognition are pattern recognition and feature extraction. The matrix Matching algorithm is used for pattern recognition. The image is then compared to the stored symbol. If the fonts used in typewritten documents are identical, pattern recognition is possible. When dealing with multilingual documents, however, pattern recognition may be difficult to use. Feature extraction does more than identify the character, it only identifies each component of it by breaking it into features.
Automated Form Population
This is an automated data entry process. Verification fields save time by storing the data in memory. OCR engines can also be improved with post-processing methods. These techniques include near neighbor analysis, which corrects any errors and highlights the words that should be written together.
Many businesses are now less burdened by the AI-based OCR tool. This allows data entry to be done quickly and efficiently without the need for tedious or lengthy processes.
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.
