Twitter has emerged as one of the world’s most widely used social networking services.  Much of  Twitter’s appeal lies in its simplicity, and in the way it fosters a sense of immediacy.    Since “tweets” are limited to 140 characters, a huge bonus is that communication on Twitter must be brief and to the point; so it doesn’t have to take up a great deal of time.  And yet, the power of sharing  on Twitter is enormous.

As is the case with so many web-based applications,  accessibility is a major problem with Twitter.  That’s why Dennis Lembree has created Accessible Twitter.  Lembree’s Twitter interface perhaps benefits blind users more than any other identifiable group, but I appreciate a comment made by Lembree in an interview posted on Accessify, “Web accessibility is for everyone, not only for the 10% or so of folks with some type of disability…” So Accessible Twitter can enhance accessibility for a wide range of Twitter users.

The site indicates that Accessible Twitter is still in “Alpha”, but it has functioned just fine for me without any glitches.  As listed on the site, the accessibility features are:

• All links are keyboard accessible.
• DM, Favorite, Reply, Re-tweet, and Delete links are all visible by default.
• Simple, consistent layout and navigation (and signed-in username always displayed).
• Headings and page titles are implemented for optimal accessibility. This includes an H3 heading (for author screen name) added to tweets to assist with screen reader users.
• Works great with or without JavaScript.
• Ajax actions are concluded with an alert that notifies the user of the result of the action.
• Large default text size and high color contrast. Layout/text resizes without breaking.
• When entering tweets, audio cues indicate when the character limit is almost reached (in additon to character counter).
• Forms and data tables are marked up for optimal accessibility.
• Code is semantic, light, and adheres to best practices in Web Standards.
• If a tweet is in response to another (and marked as such in the data), a clearer link is provided to the that tweet (not hidden). There’s also a more obvious link to the “permanent page” for the tweet.
• A clearer link is provided for the “permanent” link for a tweet; the page containing only a single tweet.
• Definitions of abbreviations are provided to certain acronyms in tweets such as IMHO (in my humble opinion).
• Tested on all major browsers: IE6, IE7, IE8, Firefox, Safari, Opera, and Chrome.

After a long blogging silence, I’m back with a free resource that I’m especially excited about.  I’ve discovered an effective free option for turning scanned text into digital text that can then be used with a wide range of study tools,  including text-to-speech.  All previous attempts to create useful digital text from the PDF files  made with standard flatbed scanners and their OCR software have yielded disappointing results.  The results with ScannedPDFtoWord aren’t perfect, but they are very very good.

ScannedPDFtoWord does exactly what its name implies.  It converts scanned PDF documents to Microsoft Word (.doc) files.  The online conversion is quick, and the quality of the text is excellent when opened in Word.  The conversion process could hardly be easier, and registration isn’t even required.

You simply upload the scanned PDF file and give an email address where you can download the converted file.  Here’s what the user interface looks like.

Although the graphics were missing, once opened in MS Word the converted files retained original formatting.  I was able to edit the scanned text easily.  For example, there was no problem in selecting the entire document and changing the font.  There were almost no errors that needed to be corrected in the scanned document.

I was especially pleased to confirm that WordTalk worked flawlessly with the scanned text.  WordTalk is the versatile free add-on for MS Word that provides text-to-speech, a talking spell checker, conversion of text to  MP3 audio format,  etc. There are, of course, numerous other study tools built into MS Word that are available to a learner once a document has been scanned into MS Word via ScannedPDFtoWord.

Below is a sample of scanned text that was converted by ScannedPDFtoWord and opened in MS Word.  I’ve highlighted the only errors I could find–the ‘f’ dropped from the word ‘first’ and the two dates, where the number 1 at the beginning of both dates was converted as the capital letter I.  As I said at the outset, this isn’t perfect; but I think it’s very very good.