What is OCR?
Introduction
Optical character recognition (OCR) technology is an effective business process that saves time, money and other resources by leveraging automated data extraction and storing capabilities. Text recognition is another term for optical character recognition (OCR). OCR software extracts and repurposes data from scanned papers, camera photos, and image image-only pdf files. OCR software extracts letters from images, and converts them to words, and then sentences, allowing access to and alteration of the original material. It also eliminates the necessity for data entering by hand.
OCR systems turn physical, printed documents into machine-readable text using a mix of hardware and software. Text is typically copied or read by hardware, such as an optical scanner or dedicated circuit board, and then advanced processing is handled by software. OCR software can use artificial intelligence AI training datasets to accomplish more complex methods of intelligent character recognition (ICR), such as distinguishing languages or handwriting styles. OCR is most typically used to convert hard copy legal or historical documents into pdf documents that users may edit, format, and search as if they were generated with a word processor. Collection of text data like OCR is a part of collecting text datasets.
What is The history of Optical Character Recognition?
Ray Kurzweil founded Kurzweil Computer Products, Inc. in 1974, with the goal of developing an Omni-font optical character recognition (OCR) system that could recognize text written in practically any typeface. He determined that the ideal use of this technology would be a machine-learning device for the blind, so he built a text-to-speech reading machine. Kurzweil sold his company to Xerox in 1980, which was interested in commercializing paper-to-computer text conversion.
While digitizing historical newspapers in the early 1990s, OCR technology became popular. Since then, technology has advanced significantly. Today’s systems are capable of delivering near-perfect OCR accuracy. Complex document processing procedures are automated using advanced approaches. Prior to the availability of OCR technology, the only way to digitally format documents was to manually retype the text. This was not only time-consuming, but it also included inaccuracies and typographical errors. OCR services are now readily available to the general public. Google cloud vision OCR, for example, is used to scan and store documents on your smartphone.
How does OCR work?
A scanner is used in optical character recognition (OCR) to process the physical form of a document. After these, pages have been copied, OCR software turns the document into two-color or black-and-white. The scanned-in image or bitmap is evaluated for lights and dark areas, with dark parts identified as characters to be recognized and light portions classified as background. The dark areas are then searched for alphabetic letters or numeric digits. This stage usually entails focusing on a single character, word, or block of text at a time. Following that, characters are detected using one of two algorithms: pattern recognition or feature recognition.
When the OCR application is fed examples of text in different fonts and formats, it compares and recognizes characters in the scanned document or picture file. Feature detection occurs when the OCR uses rules to recognize characters in a scanned document based on the features of a given letter or digit. A character’s features include the number of angled lines, crossing lines, or curves. The capital “A”, for example, is recorded as two diagonal lines intersected by a horizontal line in the center. When a character is recognized, it is turned into an ASCII code (American Standard Code for Information Interchange) that computer systems can utilize to perform additional operations. An OCR application examines the structure of a document image as well. It splits the page into elements such as text blocks, tables, and graphics. The lines are separated into words, which are then divided into characters. After identifying the characters, the algorithm compares them to a set of pattern images. The programme displays the detected text after it has processed all possible matches.
What are the benefits of Optical Character Recognition?
The fundamental advantage of optical character recognition (OCR) technology is that it streamlines data entry by allowing for simple text searches, modification, and storage. OCR enables organizations and people to keep files on their PCs, laptops, and other devices, guaranteeing that all paperwork is always available. The following are some of the advantages of using OCR technology:
Cut expenses
Workflows should be accelerated.
Document routing and content processing should be automated.
Data should be centralized and secured (no fires, break-ins or documents lost in the bank vaults)
Improve service by ensuring staff have access to the most recent and correct information
What are the use cases of OCR?
Converting printed paper documents into machine-readable text documents is the most well-known application of optical character recognition (OCR). After OCR processing, the text of a scanned paper document can be modified with a word processor such as Microsoft Word or Google Docs. OCR is frequently utilised as an unnoticed technology, powering many well-known systems and services in our daily lives. Data-entry automation, assisting blind and visually impaired people, and indexing documents for search engines, such as passports, licence plates, invoices, bank statements, business cards, and automatic number plate recognition, are all important — but lesser-known — applications for OCR technology.
OCR allows big-data modelling to be optimised by turning paper and scanned picture documents into machine-readable, searchable pdf files. Processing and retrieving relevant information cannot be automated without first using OCR in documents that lack text layers. Scannable papers can now be connected to a big-data system that can read customer data from bank statements, contracts, and other essential printed documents thanks to OCR text datasets. Organizations can use OCR to automate the input step of data mining rather than having personnel evaluate innumerable picture documents and manually enter inputs into an automated big-data processing workflow. OCR software can recognize text in images, extract text from images, and save text files in jpg, jpeg, png, BMP, tiff, pdf, and other formats.
OCR and GTS
As a global technology leader, Global Technology Solutions is always developing new and enhanced software programmes for both commercial and personal usage. We have improved our optical character recognition capabilities throughout the years by merging them with artificial intelligence (AI). We provide image data collection, text data collection, and video and speech data collection.
Simply producing document templates is no longer sufficient since businesses demand insights as well. Combining AI and OCR is proven to be a winning method for data capture since recognition software collects information while also interpreting the content. In reality, this means that AI technologies can detect errors without the assistance of a human user, resulting in more efficient fault management and time savings.
Comments
Post a Comment