Many organizational workflows receive data from print media. Business procedures include invoices, paper forms, published contracts, and scanned lawful documents. This large paperwork volume requires a lot of space and time to keep and manage records. Hence, paperless document management is the future. OCR technology solves the issue by transforming text images into data, easily analyzed by firm software.
According to Statista, the Optical Character Recognition (OCR) market share is projected to reach more than 14.5% in China.
What is Optical Character Recognition?
OCR system is an organizational solution for automating data extraction from written or printed text from an image file or scanned data. It helps alter the text into a machine-readable form that can be used for document processing, such as exploring or editing.
OCR solves the issues by converting images into data. So, companies use the extracted information to conduct analytics, automate procedures, streamline operations, and enhance productivity.
Digital OCR Working
OCR works a bit differently, but they have some standard rules and step-by-step procedures, such as:
- Image Acquisition
OCR scanner examines physical paper documents and transforms them into scanned images. The document is usually in black and white and is used to distinguish the darker (characters) and brighter (background) regions.
The OCR systems can rectify errors with advanced techniques such as binarization, de-skewing, normalization, and zoning to enhance the scanned image’s accuracy.
- Text Recognition
OCR image reader identifies original characters from scanned documents or images. Usually, it is executed through two main algorithms:
- Feature extraction
- Pattern matching
It decomposes or breaks down the glyphs into critical features such as line direction, intersection, and closed loops. Furthermore, it utilizes this feature to search for the exact match or the nearest among its stored glyphs.
It functions by separating a character image known as a glyph and comparing it with an already stored glyph. Pattern matching operates when a stored glyph has the same scale and font as the provided one. This approach works well with observed document images classified in a recognized font.
The OCR system converts the fetched information into e-documents. So, advanced techniques compare extracted data against a library of characters to maximize accuracy.
Optical Character Recognition Types
Data analysts recognize different OCR types depending on their application and use. Following are a few samples:
- Simple OCR Software
An OCR system works by keeping different text image patterns and fonts as templates. It uses different pattern-matching algorithms to contrast character-by-character text images with its inner database. If the software corresponds to a text, phrase by phrase, it is called optical word recognition. But, this technique has a few limitations, such as handwriting styles and unlimited fonts.
- Intelligent Character Recognition Software
Advanced OCR systems use ICR technology to interpret the text like humans do. They utilize new techniques that provide training to machines to behave like humans.
A machine learning method called a neural network examines the text over a few levels that process the image frequently. Moreover, it searches for diverse image particulars such as intersections, loops, curves, and lines, merging the outcome of all these different analysis levels to get the final results. ICR usually processes the image character one at a time, but the procedure is quick and obtains results within seconds.
- Intelligent Word Recognition
It works on similar principles as ICR, but this processes word images rather than pre-processing the images into characters.
- Searchable Text
Organizations easily convert their new and existing documents into searchable ones. They automatically process the text database using data analytics software to process knowledge.
- Operational Efficiency
Businesses can enhance efficiency through OCR systems to automatically merge document and digital workflows within the industry. A few examples are stated below:
- Transform handwritten documents into editable texts.
- Find the necessary data by instantly exploring a term in the database. It removes the hassle of manually sorting files in a box.
- Scan hand-filled documents for automated validation, editing, analysis, and reviews. It saves the time needed for conventional data entry and document processing.
- Artificial Intelligence Solutions
Digital OCR is a part of the artificial intelligence solutions that firms implement. For instance, it reads and scans road signs and number plates in self-driven cars, recognizes product packaging, and searches for brand symbols in social media posts. Thus, artificial technology assists organizations to make better operational and marketing decisions that minimize expenses and improve the client experience.
More than creating document templates is required, as organizations also want real-time insights. Combining OCR technology with AI proves to be a winning strategy for data capturing. In short, ocr image reader is essential in analyzing human mistakes, managing time, and saving resources.