Text And Text 2 Higher Level

[Alayna Rother]

Theintent of this Success Criterion is to ensure that visually rendered text, including text-based controls (text characters that have been displayed so that they can be seen [vs. text characters that are still in data form such as ASCII]) can be scaled successfully so that it can be read directly by people with mild visual disabilities, without requiring the use of assistive technology such as a screen magnifier. Users may benefit from scaling all content on the Web page, but text is most critical.

The scaling of content is primarily a user agent responsibility. User agents that satisfy UAAG 1.0 Checkpoint 4.1 allow users to configure text scale. The author's responsibility is to create Web content that does not prevent the user agent from scaling the content effectively. Authors may satisfy this Success Criterion by verifying that content does not interfere with user agent support for resizing text, including text-based controls, or by providing direct support for resizing text or changing the layout. An example of direct support might be via server-side script that can be used to assign different style sheets.

The author cannot rely on the user agent to satisfy this Success Criterion for HTML content if users do not have access to a user agent with zoom support. For example, if they work in an environment that requires them to use IE 6.

If the author is using a technology whose user agents do not provide zoom support, the author is responsible to provide this type of functionality directly or to provide content that works with the type of functionality provided by the user agent. If the user agent doesn't provide zoom functionality but does let the user change the text size, the author is responsible for ensuring that the content remains usable when the text is resized.

Some user interface components that function as a label and require activation by the user to access content are not wide enough to accommodate the label's content. For example, in Web mail applications the subject column may not be wide enough to accommodate every possible subject header, but activating the subject header takes the user to the full message with the full subject header. In Web-based spreadsheets, cell content that is too long to be displayed in a column can be truncated, and the full content of the cell is available to the user when the cell receives focus. The content of a user interface component may also become too wide in user interfaces where the user can resize the column width. In this type of user interface component, line wrapping is not required; truncation is acceptable if the component's full content is available on focus or after user activation and an indication that this information can be accessed, is provided to the user in some way besides the fact that it is truncated.

Content satisfies the Success Criterion if it can be scaled up to 200%, that is, up to twice the width and height. Authors may support scaling beyond that limit, however, as scaling becomes more extreme, adaptive layouts may introduce usability problems. For example, words may be too wide to fit into the horizontal space available to them, causing them to be truncated; layout constraints may cause text to overlap with other content when it is scaled larger; or only one word of a sentence may fit on each line, causing the sentence to be displayed as a vertical column of text that is difficult to read.

The working group feels that 200% is a reasonable accommodation that can support a wide range of designs and layouts, and complements older screen magnifiers that provide a minimum magnification of 200%. Above 200%, zoom (which resizes text, images, and layout regions and creates a larger canvas that may require both horizontal and vertical scrolling) may be more effective than text resizing. Assistive technology dedicated to zoom support would usually be used in such a situation and may provide better accessibility than attempts by the author to support the user directly.

Images of text do not scale as well as text because they tend to pixelate, and therefore we suggest using text wherever possible. It is also harder to change foreground and background contrast and color combinations for images of text, which are necessary for some users.

Each numbered item in this section represents a technique or combination of techniques that the WCAG Working Group deems sufficient for meeting this Success Criterion. However, it is not necessary to use these particular techniques. For information on using other techniques, see Understanding Techniques for WCAG Success Criteria, particularly the "Other Techniques" section.

Ensuring that text containers resize when the text resizes AND using measurements that are relative to other measurements in the content by using one or more of the following techniques:

Although not required for conformance, the following additional techniques should be considered in order to make content more accessible. Not all techniques can be used or would be effective in all situations.

functionality provided by assistive technology includes alternative presentations (e.g., as synthesized speech or magnified content), alternative input methods (e.g., voice), additional navigation or orientation mechanisms, and content transformations (e.g., to make tables more accessible).

The distinction between mainstream user agents and assistive technologies is not absolute. Many mainstream user agents provide some features to assist individuals with disabilities. The basic difference is that mainstream user agents target broad and diverse audiences that usually include people with and without disabilities. Assistive technologies target narrowly defined populations of users with specific disabilities. The assistance provided by an assistive technology is more specific and appropriate to the needs of its target users. The mainstream user agent may provide important functionality to assistive technologies like retrieving Web content from program objects or parsing markup into identifiable bundles.

Captions are similar to dialogue-only subtitles except captions convey not only the content of spoken dialogue, but also equivalents for non-dialogue audio information needed to understand the program content, including sound effects, music, laughter, speaker identification and location.

Determined from technology-specific data structures in a non-markup language and exposed to assistive technology via an accessibility API that is supported by commonly available assistive technology.

The following are Test Rules for certain aspects of this Success Criterion. It is not necessary to use these particular Test Rules to check for conformance with WCAG, but they are defined and approved test methods. For information on using Test Rules, see Understanding Test Rules for WCAG Success Criteria.

The content was developed as part of the WAI-Core projects funded by U.S. Federal funds. The user interface was designed by the Education and Outreach Working Group (EOWG) with contributions from Shadi Abou-Zahra, Steve Lee, and Shawn Lawton Henry as part of the WAI-Guide project, co-funded by the European Commission.

I want the text to align with the top of the cell; that is, I don't want any blank space between the top of the cell and the text, and I want the row height to be just enough to accommodate the text. Here's what I've tried:

I just had this happen in a cell. What I did was reformat the cell. The icon is in the Home tab under font. I clicked on the cell, reformatted it and the text popped up to the top of the cell. Don't know what was in the cell to keep text from moving to top. But reformatting did the trick.

Mathematically, an embedding space, or latent space, is defined as a manifold in which similar items are positioned closer to one another than less similar items. In this case, sentences that are semantically similar should have similar embedded vectors and thus be closer together in the space.

In these cases, you can pre-calculate the embeddings for your targets (i.e. the documents you want to search or examples for classification) and store them in an indexed database. This lets you capture the powerful natural language understanding of deep neural models as text embeddings as you add new items to your database, then run your search or classifier without expensive GPU compute.

Imagine a two-dimensional floor plan of a single-story library. Our library-goers are all cat lovers, dog lovers, or somewhere in between. We want to shelve cat books near other cat books and dog books near other dog books.

In English, a vocabulary of something like 30,000 words works pretty well for this kind of bag-of-words model. In a computational world, we can scale these dimensions up more smoothly than we could in the case of brick-and-mortar libraries, but the problem is similar in principle. Things just get unwieldy at these high dimensions. Algorithms grind to a halt as the combinatorics explode, and the sparsity (most documents will have a count of 0 for most terms) is problematic for statistics and machine learning.

In the examples above, we were using word counts as a proxy for some more nebulous idea of topicality. By projecting those word counts down into an embedding space, we can both reduce the dimensionality and infer latent variables that indicate topicality better than the raw word counts. To do this, though, we need a well-defined algorithm like LSA that can process a corpus of documents to find a good mapping between our bag-of-words input and vectors in our embedding space.

The basic concept of a recurrent neural network (RNN) is that each token (usually a word or word piece) in our sequence feeds forward into the representation of our next one. We start with the embedding for our first token t0. For the next token, t1 we take some function (defined by the weights our neural network learns) of the embeddings for t0 and t1 like f(t0, t1). Each new token combines with the previous token in the sequence until we reach the final token, whose embedding is used to represent the whole sequence. This simple version of this architecture is a fully-recurrent neural network (FRNN).

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