Jaws software introduction
2 About JAWS.pdf.pdf
Page – 1
JAWS ( screen reader )
JAWS ( Job Access With Speech ) is a computer screen reader program for Microsoft Windows that allows blind and visually impaired users to read the screen either with a text – to – speech output or by a Refreshable Braille display. JAWS is produced by the Blind and Low Vision Group of Freedom Scientific, St. Petersburg, Florida, USA. A July 2015 screen reader user survey by WebAIM, a web accessibility company found JAWS to be the most popular screen reader worldwide: 30. 2 % of survey participants used it as a primary screen reader, while 43. 7 % of participants used it often. This level of usage is significantly lower than that found in the January 2014 survey, where the respective figures for JAWS were 50 % and 63. 9 %
AWS was originally released in 1989 by Ted Henter, a former motorcycle racer who lost his sight in a 1978 automobile accident. In 1985, Henter, along with a US $ 180, 000 investment from Bill Joyce, founded the Henter – Joyce Corporation in St. Petersburg. Florida. Joyce sold his interest in the company back to Henter in 1990. In April 2000, Henter – Joyce, Blazie Engineering, and Arkenstone, Inc. merged to form Freedom Scientific
JAWS was originally created for the MS – DOS operating system. It was one of several screen readers giving blind users access to text – mode MS – DOS applications. A feature unique to JAWS at the time was its use of cascading menus, in the style of the popular Lotus 1 – 2 – 3 application. What set JAWS apart from other screen readers of the era was its use of macros that allowed users to customize the user interface and work better with various applications. Ted Henter and Rex Skipper wrote the original JAWS code in the mid – 1980s, releasing version 2. 0 in mid – 1990. Skipper left the company after the release of version 2. 0, and following his departure, Charles Oppermann was hired to maintain and improve the product. Oppermann and Henter regularly added minor and major features and frequently released new versions. Freedom Scientific now offers JAWS for MS – DOS as a freeware download from their web site. In 1993, Henter – Joyce released a highly modified version of JAWS for people with learning disabilities. This product, called WordScholar, is no longer available.
JAWS for Windows [ edit ] In 1992, as Microsoft Windows became more popular, Oppermann began work on a new version of JAWS. A principal design goal was not to interfere with the natural user interface of Windows and to continue to provide a strong macro facility. Test and beta versions of JAWS for Windows ( JFW ) were shown at conferences throughout 1993 and 1994. During this time, developer Glen Gordon started working on the code, ultimately taking over its development when Oppermann was hired by Microsoft in November 1994. Shortly afterwards, in January 1995, JAWS for Windows 1. 0 was released. Currently a new revision of JAWS for Windows is released about once a year, with minor updates in between. The latest version is 17. 0, released in October 2015.Page – 2
JAWS ‘ feature set and configurability have been described as ” complex “, with training recommended for users such as web designers performing accessibility testing, to avoid drawing the wrong conclusions from such testing.
Release history Version Release date
Significant changes
JFW 1. 0
January 1995
First version for Windows, supported Windows 3. 1 and Windows for Workgroups 3. 11
JFW 2. 0
About 1996
Added support for Windows 95
JFW 4. 0
September 14, 2001
Many changes to user interface Optional tutor and access key help added
JFW 4. 5
August 30, 2002
JFW 5. 0
October 9, 2003
Quick navigation keys added to Internet Explorer, for navigating between HTML elements on a page Many improvements with Internet support Speech and Sounds Manager, for indication of fonts, controls and web page elements. Introduction of Internet licensing Introduction of separate user settings Comes with a demo of FSReader, a DAISY reader manufactured by Freedom Scientific, for reading JAWS training material Release of thumb drive version Support for Mozilla Firefox among other applications
JFW 6. 0
March 3, 2005
JFW 7. 0
October 14, 2005 – 0
JFW 7. 1
June 21, 2006
JFW 8. 0
November 17, 2006
Automatic updates Switched to a Document Object Model engine for HTML rendering Ships with RealSpeak Solo SAPI 5 Speech Synthesizers No longer supports Windows 98 or Windows ME Supports Windows Vista HTML composition support New ” adjust JAWS options ” dialog box Introduction of JAWS Tandem, to allow a person using JAWS to access another computer running JAWS, much likeRemote Desktop Support for iTunes version 8 and the iTunes Store
JFW 9. 0
November 19. 2007
JFW 10. 0
November 3, 2008Page – 3
JFW 11. 0 October 23,
2009
JFW 12. 0
October 21. 2010
JFW 13. 0
October 24, 2011
JFW 14. 01
October 22, 2012
Research It, a feature that provides quick access to information such as word definitions, weather forecasts and sports scores Now comes on a DVD rather than a CD Ships with the full version of FSReader 2. 0 rather than a demo Provides a settings center to replace the old configuration manager to easily change settings Allows consistent navigation of ribbons using the ” virtual ribbon menu ” Supports the ARIA specification for making webpages more accessible Provides in – built optical character recognition for graphics on the screen Provides a new ” Quick settings ” feature as a replacement for the old ” Adjust JAWS options ” dialog box Supports Windows 8 Introduces Flexible Web, a feature that allows the user to hide unwanted HTML elements on webpages or to go to a particular HTML element when a page launches Supports the touch screen in Windows 8 Comes with FS Reader 3. 0, which can read DAISY files as if they ‘ re HTML documents. Introduces ” JAWS command search “, a feature that allows the user to search for JAWS commands. Introduces optical character recognition for PDF documents Supports MathML No longer supports Windows XP Supports Smart Navigation, an alternative method of navigating links and tables in webpages Uses Liblouis, an open – source Braille translator, for updated support of Unified English Braille
JFW 15. 0
October 28, 2013
JFW
- 0 October 28, 2014 JFW 17. 0 October 29, 2015 JFW 13. 0 Optical character recognition ( optical character reader, OCR ) is the mechanical or electronic conversion of images of typed, hand written or printed text into machine – encoded text, whether from a scanned document, a photo of a document, a scene – photo ( for example the text on signs and billboards in a landscape photo ) or from subtitle text superimposed on an image ( for example from a television broadcast ). It is widely used as a form of information entry from printed paper data records, whether passport documents, invoices, bankPage – 4 statements, computerised receipts, business cards, mail, printouts of static – data, or any suitable documentation. It is a common method of digitising printed texts so that they can be electronically edited, searched, stored more compactly displayed on – line, and used in machine processes such as cognitive computing, machine translation, ( extracted ) text – to – speech, key data andtext mining OCR is a field of research in pattern recognition, artificial intelligence and computer vision. History Early optical character recognition may be traced to technologies involving telegraphy and creating reading devices for the blind. In 1914, Emanuel Goldberg developed a machine that read characters and converted them into standard telegraph code. Concurrently, Edmund Fournier d ‘ Albe developed the Optophone, a handheld scanner that when moved across a printed page, produced tones that corresponded to specific letters or characters. In the late 1920s and into the 1930s Emanuel Goldberg developed what he called a ” Statistical Machine ” for searching microfilm archives using an optical code recognition system. In 1931 he was granted USA Patent number 1. 838. 389 for the invention. The patent was acquired by IBM With the advent of smart – phones and smartglasses, OCR can be used in internet connected mobile device applications that extract text captured using the device ‘ s camera. These devices that do not have OCR functionality built – in to the operating system will typically use an OCR API to extract the text from the image file captured and provided by the device. The OCR API returns the extracted text, along with information about the location of the detected text in the original image back to the device app for further processing ( such as text – to – speech ) or display. Applications OCR engines have been developed into many kinds of object – oriented OCR applications, such as receipt OCR, invoice OCR, check OCR, legal billing document OCR. They can be used for Data entry for business documents, e. g. check, passport, invoice, bank statement and receipt Automatic number plate recognition Automatic insurance documents key information extraction Extracting business card information into a contact list More quickly make textual versions of printed documents, e. g. book scanning for Project Gutenberg Make electronic images of printed documents searchable, e. g. Google Books Converting handwriting in real time to control a computer ( pen computing ) Defeating CAPTCHA anti – bot systems, though these are specifically designed to prevent OCR Assistive technology for blind and visually impaired users • Types [ edit ] • Optical character recognition ( OCR ) – targets typewritten text, one glyph or character at a time Optical word recognition – targets typewritten text, one word at a time ( for languages that use a space as a word divider ). ( Usually just called ” OCR “. )Page – 5 Intelligent character recognition ( ICR ) – also targets handwritten printscript or cursive text one glyph or character at a time, usually involving machine learning. Intelligent word recognition ( IWR ) – also targets handwritten printscript or cursive text, one word at a time. This is especially useful for languages where glyphs are not separated in cursive script. OCR is generally an ” offline ” process, which analyses a static document. Handwriting movement analysis can be used as input to handwriting recognition. Instead of merely using the shapes of glyphs and words, this technique is able to capture motions, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This additional information can make the end – to – end process more accurate. This technology is also known as ” on – line character recognition “. ” dynamic character recognition “. ” real – time character recognition “, and ” intelligent character recognition “. Techniques [ edit ] Pre – processing [ edit ] OCR software often ” pre – processes ” images to improve the chances of successful recognition. Techniques include: De – skew – If the document was not aligned properly when scanned, it may need to be tilted a few degrees clockwise or counterclockwise in order to make lines of text perfectly horizontal or vertical. Despeckle – remove positive and negative spots, smoothing edges Binarisation – Convert an image from color or greyscale to black – and – white ( called a ” binary image ” because there are two colours ). The task of binarisation is performed as a simple way of separating the text ( or any other desired image component ) from the background. The task of binarisation itself is necessary since most commercial recognition algorithms work only on binary images since it proves to be simpler to do so. 1 In addition, the effectiveness of the binarisation step influences to a significant extent the quality of the character recognition stage and the careful decisions are made in the choice of the binarisation employed for a given input image type, since the quality of the binarisation method employed to obtain the binary result depends on the type of the input image ( scanned document, scene text image, historical degraded document etc. ). Line removal – Cleans up non – glyph boxes and lines Layout analysis or ” zoning ” – Identifies columns, paragraphs, captions, etc. as distinct blocks. Especially important in multi – column layouts and tables Line and word detection – Establishes baseline for word and character shapes, separates words if necessary. Script recognition – In multilingual documents, the script may change at the level of the words and hence, identification of the script is necessary, before the right OCR can be invoked to handle the specific script.Page – 6 Character isolation or ” segmentation ” – For per – character OCR, multiple characters that are connected due to image artifacts must be separated; single characters that are broken into multiple pieces due to artifacts must be connected. Normalise aspect ratio and scale Segmentation of fixed – pitch fonts is accomplished relatively simply by aligning the image to a uniform grid based on where vertical grid lines will least often intersect black areas. For proportional fonts, more sophisticated techniques are needed because whitespace between letters can sometimes be greater than that between words, and vertical lines can intersect more than one character. Character recognition [ edit ] There are two basic types of core OCR algorithm, which may produce a ranked list of candidate characters. Matrix matching involves comparing an image to a stored glyph on a pixel – by – pixel basis; it is also known as ” pattern matching “, ” pattern recognition “, or ” image correlation “. This relies on the input glyph being correctly isolated from the rest of the image, and on the stored glyph being in a similar font and at the same scale. This technique works best with typewritten text and does not work well when new fonts are encountered. This is the technique the early physical photocell – based OCR implemented, rather directly. Feature extraction decomposes glyphs into ” features like lines, closed loops, line direction, and line intersections. These are compared with an abstract vector – like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer visionare applicable to this type of OCR, which is commonly seen in ” intelligent ” handwriting recognition and indeed most modern OCR software. iz Nearest neighbour classifiers such as the k – nearest neighbors algorithm are used to compare image features with stored glyph features and choose the nearest match. Software such as Cuneiform and Tesseract use a two – pass approach to character recognition. The second pass is known as ” adaptive recognition ” and uses the letter shapes recognised with high confidence on the first pass to recognise better the remaining letters on the second pass. This is advantageous for unusual fonts or low – quality scans where the font is distorted ( e. g. blurred or faded ) The OCR result can be stored in the standardised ALTO format, a dedicated XML schema maintained by the United States Library of Congress. Post – processing [ edit ] OCR accuracy can be increased if the output is constrained by a lexicon – a list of words that are allowed to occur in a document. This might be, for example, all the words in the English language, or a more technical lexicon for a specific field. This technique can be problematic if the document contains words not in the lexicon, like proper nouns. Tesseract uses its dictionary to influence the character segmentation step. for improved accuracy. The output stream may be a plain text stream or file of characters, but more sophisticated OCR systems can preserve the original layout of the page and produce, for example, anPage – 7 annotated PDF that includes both the original image of the page and a searchable textual representation. ” Near – neighbor analysis ” can make use of co – occurrence frequencies to correct errors, by noting that certain words are often seen together. For example, ” Washington, D. C. ” is generally far more common in English than ” Washington DOC “. Knowledge of the grammar of the language being scanned can also help determine if a word is likely to be a verb or a noun, for example, allowing greater accuracy. The Levenshtein Distance algorithm has also been used in OCR post – processing to further optimize results from an OCR API.
