The term OCR (Optical Character Recognition) might ring a bell, yet understanding its functions could be somewhat challenging. In a nutshell, OCR is synonymous with text extraction, primarily turning an image to a text file. The technology is widely employed by enterprises for various tasks, including data capture from receipts, information extraction from documents or even reading license plates.
An Insight on OCR!
The necessity for accruing precise data for business purposes has never been greater. OCR happens to be a formidable method that aids data collection while eliminating the necessity for manual work. Countless OCR software solutions exist today, each specially crafted to cater to text extraction and data recognition. Each of them serve as a reliable image to text converter for extracting text from scanned documents and images, subsequently converting them into machine-readable files. Card scanning features conveniently enable image to text file transformation without any formatting disruptions, with users able to effortlessly copy text from an image online.
Understanding OCR
OCR is an ideal method for automating the process of extracting data from handwritten, printed, or scanned documents, image files, and transforming them into a format that is compatible with machines. The extracted data is commonly utilized for processing tasks like editing or searching.
The OCR Operation
Though OCR programs may differ slightly in their operation, they nonetheless adhere to certain fundamental guidelines. They include:
Image Capture:
In this stage, an image text scanner scans physical documents and transforms them into scanned images. This phase primarily involves rendering files in black and white, facilitating the distinction of brighter (background) and darker (characters/elements) aspects.
Pre-processing:
OCR technology ensures error rectification in this phase, utilizing methods such as de-skewing, binarization, zoning, and normalizing to maximize image accuracy.
Text Recognition:
AI tools accurately decipher the original characters/elements from scanned photos or documents. This operation primarily relies on pattern matching and feature extraction algorithms.
Post-processing:
Impressively, following text extraction, OCR applications swiftly convert the data into electronic documents. Advanced OCR systems can compare the data with a character library or glossary for optimal accuracy.
The OCR Spectrum
Diverse OCR features exist, each with its own unique strengths. They comprise:
- OCR (Optical Character Recognition): Recognizes handwritten or typed characters through an internal database.
- Word Recognition (OWR): Also known as OCR, it targets typeset text or specific words, proving useful for languages that use spaces to separate words.
- Optical Mark Recognition (OMR): Efficiently identifies logos, symbols, watermarks, and specific patterns on paper documents.
- Intelligent Character Recognition (ICR): This advanced system employs data capture tools to read handwritten or cursive text. The significant benefits provided by ICR are its use of AI and machine learning technologies that analyze aspects of text such as curves, lines, and loops. Moreover, this system can identify and process individual characters one at a time.
