Differences between ICR and OCR
OCR
Optical character recognition (OCR) is commonly considered to apply to any recognition technique that reads machine printed text. An example of a traditional OCR use case would be to translate the characters from an image of a printed document, such as a book page, newspaper clipping, or legal contract, into a separate file that could be searched and updated with a word processor or document viewer. It's also quite helpful for automating the processing of forms. Information can be swiftly extracted from form fields and entered into another application, like a spreadsheet or database, by zonally applying the OCR engine to those fields.
Yet, data is typically manually input rather than typed into form fields. Character identification becomes even more challenging while reading handwritten material. The diversity of more than 700,000 printed font variants is tiny compared to the near unlimited variations in hand-printed characters. The recognition program must take into account not just stylistic differences but also the kind of writing implement used, the standard of the paper, errors, hand stability, and smudges or running ink. [2]
ICR
Intelligent character recognition (ICR) makes use of continuously improving algorithms to collect more information about the variances in hand-printed characters and more precisely identify them. ICR, which was created in the early 1990s to aid in the automation of forms processing, enables the conversion of manually entered data into text that is simple to read, search for, and change. When used to read characters that are obviously divided into distinct areas or zones, such as fixed fields seen on many structured forms, it works best. [2]
Both OCR and ICR can be configured to read a variety of languages; however, limiting the expected character set to a smaller number of languages will produce better recognition outcomes. ICR cannot read cursive handwriting since it must still be able to assess each character individually. While writing in cursive, it might be difficult to tell where one character ends and another one begins, and there are more differences across samples than when hand-printing text. A more recent method called intelligent word recognition (IWR) focuses on reading a word in context rather than recognizing individual characters. [2]
Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo or from subtitle text superimposed on an image.
Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available. A handwriting recognition system handles formatting, performs correct segmentation into characters, and finds the most possible words.
Handwriting is the writing done with a writing instrument, such as a pen or pencil, in the hand. Handwriting includes both block and cursive styles and is separate from formal calligraphy or typeface. Because each person's handwriting is unique and different, it can be used to verify a document's writer. The deterioration of a person's handwriting is also a symptom or result of several different diseases. The inability to produce clear and coherent handwriting is also known as dysgraphia.
Optical mark recognition (OMR) collects data from people by identifying markings on a paper. OMR enables the hourly processing of hundreds or even thousands of documents. For instance, students may remember completing quizzes or surveys that required them to use a pencil to fill in bubbles on paper. A teacher or teacher's aide would fill out the form, then feed the cards into a system that grades or collects data from them.
Cursive is any style of penmanship in which characters are written joined in a flowing manner, generally for the purpose of making writing faster, in contrast to block letters. It varies in functionality and modern-day usage across languages and regions; being used both publicly in artistic and formal documents as well as in private communication. Formal cursive is generally joined, but casual cursive is a combination of joins and pen lifts. The writing style can be further divided as "looped", "italic", or "connected".
Newton OS is a discontinued operating system for the Apple Newton PDAs produced by Apple Computer, Inc. between 1993 and 1997. It was written entirely in C++ and trimmed to be low power consuming and use the available memory efficiently. Many applications were pre-installed in the ROM of the Newton to save on RAM and flash memory storage for user applications.
Block letters are a sans-serif style of writing Latin script in which the letters are individual glyphs, with no joining.
Document processing is a field of research and a set of production processes aimed at making an analog document digital. Document processing does not simply aim to photograph or scan a document to obtain a digital image, but also to make it digitally intelligible. This includes extracting the structure of the document or the layout and then the content, which can take the form of text or images. The process can involve traditional computer vision algorithms, convolutional neural networks or manual labor. The problems addressed are related to semantic segmentation, object detection, optical character recognition (OCR), handwritten text recognition (HTR) and, more broadly, transcription, whether automatic or not. The term can also include the phase of digitizing the document using a scanner and the phase of interpreting the document, for example using natural language processing (NLP) or image classification technologies. It is applied in many industrial and scientific fields for the optimization of administrative processes, mail processing and the digitization of analog archives and historical documents.
Automatic identification and data capture (AIDC) refers to the methods of automatically identifying objects, collecting data about them, and entering them directly into computer systems, without human involvement. Technologies typically considered as part of AIDC include QR codes, bar codes, radio frequency identification (RFID), biometrics, magnetic stripes, optical character recognition (OCR), smart cards, and voice recognition. AIDC is also commonly referred to as "Automatic Identification", "Auto-ID" and "Automatic Data Capture".
Noisy text analytics is a process of information extraction whose goal is to automatically extract structured or semistructured information from noisy unstructured text data. While Text analytics is a growing and mature field that has great value because of the huge amounts of data being produced, processing of noisy text is gaining in importance because a lot of common applications produce noisy text data. Noisy unstructured text data is found in informal settings such as online chat, text messages, e-mails, message boards, newsgroups, blogs, wikis and web pages. Also, text produced by processing spontaneous speech using automatic speech recognition and printed or handwritten text using optical character recognition contains processing noise. Text produced under such circumstances is typically highly noisy containing spelling errors, abbreviations, non-standard words, false starts, repetitions, missing punctuations, missing letter case information, pause filling words such as “um” and “uh” and other texting and speech disfluencies. Such text can be seen in large amounts in contact centers, chat rooms, optical character recognition (OCR) of text documents, short message service (SMS) text, etc. Documents with historical language can also be considered noisy with respect to today's knowledge about the language. Such text contains important historical, religious, ancient medical knowledge that is useful. The nature of the noisy text produced in all these contexts warrants moving beyond traditional text analysis techniques.
TeleForm is a form of processing applications originally developed by Cardiff Software and now is owned by OpenText.
Recognition may refer to:
Intelligent Word Recognition, or IWR, is the recognition of unconstrained handwritten words. IWR recognizes entire handwritten words or phrases instead of character-by-character, like its predecessor, optical character recognition (OCR). IWR technology matches handwritten or printed words to a user-defined dictionary, significantly reducing character errors encountered in typical character-based recognition engines.
Forms processing is a process by which one can capture information entered into data fields and convert it into an electronic format. This can be done manually or automatically, but the general process is that hard copy data is filled out by humans and then "captured" from their respective fields and entered into a database or other electronic format.
Datacap, a privately owned company, manufactures and sells computer software, and services. Datacap's first product, Paper Keyboard, was a "forms processing" product and shipped in 1989. In August 2010, IBM announced that it had acquired Datacap for an undisclosed amount.
Handwritten biometric recognition is the process of identifying the author of a given text from the handwriting style. Handwritten biometric recognition belongs to behavioural biometric systems because it is based on something that the user has learned to do.
Scan-Optics LLC, founded in 1968, is an enterprise content management services company and optical character recognition (OCR) and image scanner manufacturer headquartered in Manchester, Connecticut.
Sayre's paradox is a dilemma encountered in the design of automated handwriting recognition systems. A standard statement of the paradox is that a cursively written word cannot be recognized without being segmented and cannot be segmented without being recognized. The paradox was first articulated in a 1973 publication by Kenneth M. Sayre, after whom it was named.
IBM designed, manufactured and sold optical mark and character readers from 1960 until 1984. The IBM 1287 is notable as being the first commercially sold scanner capable of reading handwritten numbers.
Smart data capture (SDC), also known as 'intelligent data capture' or 'automated data capture', describes the branch of technology concerned with using computer vision techniques like optical character recognition (OCR), barcode scanning, object recognition and other similar technologies to extract and process information from semi-structured and unstructured data sources. IDC characterize smart data capture as an integrated hardware, software, and connectivity strategy to help organizations enable the capture of data in an efficient, repeatable, scalable, and future-proof way. Data is captured visually from barcodes, text, IDs and other objects - often from many sources simultaneously - before being converted and prepared for digital use, typically by artificial intelligence-powered software. An important feature of SDC is that it focuses not just on capturing data more efficiently but serving up easy-to-access, actionable insights at the instant of data collection to both frontline and desk-based workers, aiding decision-making and making it a two-way process.
