Intelligent Word Recognition: Fundamentals and Applications

By Fouad Sabry

What Is Intelligent Word Recognition

The process of recognizing unconstrained handwritten words is known as Intelligent Word Recognition, abbreviated IWR. Instead than recognizing handwritten words or phrases character by character like its predecessor, optical character recognition (OCR), IWR can recognize full handwritten words or phrases. IWR technology compares written or handwritten words to a vocabulary that has been created by the user, which considerably reduces the number of character errors that are produced by conventional character-based recognition engines.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Intelligent word recognition

Chapter 2: Optical character recognition

Chapter 3: Handwriting recognition

Chapter 4: Optical mark recognition

Chapter 5: Intelligent character recognition

Chapter 6: Document processing

Chapter 7: Automatic identification and data capture

Chapter 8: Noisy text analytics

Chapter 9: Forms processing

Chapter 10: Handwritten biometric recognition

(II) Answering the public top questions about intelligent word recognition.

(III) Real world examples for the usage of intelligent word recognition in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of intelligent word recognition' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of intelligent word recognition.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 6, 2023

Book preview

Chapter 1: Intelligent word recognition

Instead of relying on random guesses as regular character-based recognition engines do, IWR technology instead compares written or printed words to a user-defined dictionary.

Combine IWR, OCR, and ICR into one piece of software, and you have a powerful tool for processing documents, whether they are limited (printed by hand) or unlimited (produced by machine) (freeform cursive). In addition, IWR automates the process by reducing the need for human keying of previously hand-keyed data from handwritten papers.

Each word is broken down into its constituent graphemes when cursive handwriting is taken into account by the algorithm. Letters are made up of a variety of curves, forms, and lines, all of which are taken into account by IWR as it determines how confident it may be in its determination of the target word.

{End Chapter 1}

Chapter 2: Optical character recognition

From a scanned document, a photo of the document, a scene-photo (such as the text on signs and billboards in a landscape photo), or subtitle text superimposed on an image, optical character recognition (OCR) is the electronic or mechanical conversion of images of typed, handwritten, or printed text into machine-encoded text (for example: from a television broadcast).

It is a common method of digitizing printed texts for electronic editing, searching, compact storage, online display, and use in machine processes like cognitive computing, machine translation, (extracted) text-to-speech, and other suitable documentation, such as passport documents, invoices, bank statements, computerized receipts, business cards, mail, printouts of static-data, and other suitable documentation. Pattern recognition, AI, and computer vision all contribute to OCR.

Earlier versions only supported a single typeface and required training with photos of each character. Modern systems often handle many digital picture file formats and can provide a high level of identification accuracy for most typefaces. Some implementations may generate a copy of the page with all the formatting details preserved, such as graphics, columns, and other non-textual elements.

Telegraphy and the development of reading aids for the blind are two possible antecedents of modern optical character recognition.

For examining microfilm archives using an optical code recognition system, Emanuel Goldberg created what he dubbed a Statistical Machine in the 1920s and 1930s. In 1931, he received U.S. Patent 1,838,389 for his creation. IBM now owns the patent.

After developing omni-font OCR, which could read text written in almost any typeface, Ray Kurzweil founded Kurzweil Computer Products, Inc. in 1974. (Kurzweil is often credited with inventing omni-font OCR, but it was in use by companies, including CompuScan, in the late 1960s and 1970s.) The optimum use for this technology, Kurzweil reasoned, would be to develop a reading machine for the blind, which would enable the visually impaired to have a computer read aloud whatever text the user inputs. Two key technologies, the CCD flatbed scanner and the text-to-speech synthesizer, had to be developed for this gadget to become a reality. The final product was introduced during a press conference on January 13, 1976, chaired by Kurzweil and the National Federation of the Blind. The first commercial version of the optical character recognition software was released by Kurzweil Computer Products in 1978. As one of the first adopters, LexisNexis purchased the software in order to include news articles and legal briefs into its fledgling online databases. Kurzweil sold his firm to Xerox two years later because of the latter's desire to commercialize text conversion from paper to computer. Scansoft, which had been spun off by Xerox, later merged with Nuance Communications.

On the 2000s, OCR was made accessible in the cloud, on mobile devices, and in real-time