Some Must-Know Facts About Optical Character Recognition Technology
The use of technologies to differentiate typed or handwritten text characters within digital representations of actual documents is known as optical character recognition. OCR is a technology that analyzes a paper’s text and translates the characters into a language understood by computers so that data processing could be performed.
Text recognition is a term that is often used to refer to OCR. Optical character recognition systems consist of an efficient hardware and software mixture that converts physical records into machine-readable text. Text is copied or read using hardware such as an OCR scanner, whereas machines usually handle advanced processing. Artificial intelligence can be used in software to apply more advanced ways of intelligent character recognition (ICR), such as distinguishing languages or handwriting patterns.
The most popular use of optical character recognition is to convert old hard copies into editable PDFs. Users can edit, format, and scan the text until it has been saved as a soft copy. Moreover, separate containers for users to write each character in directions known as comb fields are often used on forms designed to be interpreted by OCR, encouraging people to keep characters apart and write legibly.
What is Optical Character Recognition?
OCR is the abbreviation for “Optical Character Recognition.” OCR is a text recognition system that identifies text in digital images. It’s most widely used to identify text in scanned papers, but it can also be used for various other tasks. OCR solution locates and recognizes characters such as words, digits, and symbols in a digital file. Some optical character recognition applications can automatically export the text, while others translate the characters to editable text. Advanced OCR program can export the text’s size and formatting and the text’s style on a page.
How Optical Character Recognition Works?
The actual structure of a document is processed using a scanner in the first phase of OCR. OCR service transforms the text into black and white format after all pages have been copied. The white and black areas of the scanned image or bitmap are recognized. Dark areas are recognized as characters, while white areas as the background.
The dark areas are then analyzed further to determine if they include alphabetic characters or numbers. OCR systems use various methods, but the most focus is on one character, expression, or sentence at a time. After that, computational algorithms are utilized to recognize the characters:
- Pattern recognition: OCR systems are fed samples of text in various fonts and styles, which are then compared and recognized as characters in the scanned paper.
- Feature detection: To identify characters in a scanned text, optical character recognition programs use rules based on the features of a given letter or number. The number of curved and crossed lines are examples of features. For instance, the capital letter “A” may be represented as two diagonal lines that intersect in the middle of a horizontal line.
When a symbol is recognized, it is translated into an ASCII language that computer programs may use to perform further operations. Before storing a document for future reference, users can fix simple mistakes, proofread it, and double-check if the complicated layouts were done correctly.
OCR Solutions: What’s Holding Them Back?
Letter misinterpretation, missing unreadable words, and mixing characters from various columns or picture captions are all examples of bad OCR text scanning performance. Although several factors influence the success of optical character recognition systems, the majority of errors are caused by poor text quality. Since each alphabet or icon has a wide variety of formats, styles, and shades, an OCR reader may misunderstand high-quality papers.
The following are the drawbacks that prevent OCR technology from reaching 100% accuracy:
Organized Data
The identification of organized data may be a major problem for OCR technologies. Optical character recognition technology uses many machine learning methods to identify structured data. It could be a hassle and an unwelcome need for some businesses.
Cursive Language Forms
OCR technologies can have a harder time recognizing certain languages or writing styles,e.g., in the Arabic language, recognizing its cursive form can be difficult. With that said, it must be noted that OCR can recognize characters from almost all the world’s official languages.
Colored Patterns
OCR technology works well against black and white backgrounds. This isn’t to say that the vibrant background textures are undetectable, but they do make OCR data retrieval complicated.
Blurriness
For both people and machines, blurry or glared images are quite challenging to recognize. Furthermore, OCR may have difficulty correctly identifying data on distorted or non-oriented records.
