பாலியல் தாக்குதல், வால்லுரவு, அத்துமீரல் பற்றி மார்சு 2018-இல் பாடகி சின்மயி வெளியிட்ட கானொளி. #MeTooIndia என்பாது பாரதி காண்ட பெண்ணியத்தை முதன்மையாக்குமோ ?
நானும் பாலியல் துன்புறுத்தலுக்கு ஆளானேன் பாடகி சின்மயி திடீர் என வெளியிட்ட வீடியோ! Chinmayi Sripada
Well, this blog post is not about any famous cat-fight: ‘sabaash – sariyaane potTi!’
Frame grab from the song ‘Kannum Kannum’ from movie ‘Vanjikottai Vaaliban’ with danseuse rivals Padmini and Vaijayanthimala.
but about the more mundane issue of resolution of Tamil letters that maybe affecting visual acuity and usage in practical things like billboards. Yes, we know Latha (is Tamil font from Microsoft) and Bamini famous storied font created in 1980s. Bamini font is also used in the Chennai Metro, Colombo railway station among other places; creator of Bamini was recently felicitated with 2017 Tamil Computing award for the pioneering efforts in the dawn of digital era.
Back to resolution; ‘kannu theriyithaa ?’ is the usual expression but it really asks the question are you able to see the object/thing/place/person – and not literally ‘do you have vision?’
In optical science, it is well known that free-space – distance – acts as a filter introducing blurs into the image. This is the mechanism behind why we don’t see details of far-away billboards and they grow in detail as one may approach them.
Tamil billboard; credits – masanori_jpn via Flickr.
So if you are advertising in large billboards, obviously you want to be visible to audiences as far as laws of physics [Rayleigh resolution limit] will allow.
We can gather from simple considerations the following:
So you can ask, if all the billboards are painted in large bold Tamil letters ‘adikkira maathiri’, will be visible for very long distances ? Yes. And they will also be boring.
This knowledge does not help us to choose between two fonts, since we can draw/write/pain letters in their character in any size and color – just the shape remains fixed.
Now to properly analyze the two fonts for best visual acuity, we may consider the following criteria:
Before we start drawing conclusions you also want the test subjects to have 20/20 vision or wear corrective prescription eye glasses for the same level of vision.
Now, regardless of the color and size of the fonts we can use the criteria to compare the acuity of the fonts.
But wait, can we do this by computer modeling without paint, labor and 20/20 vision subjects? You betcha! This will be subject of next blog post.
Until then…. Vaazhga Valamudan.
-Muthu
Project Madurai corpus contains a treasure trove of Tamil data across many generations and inflections of Tamil language. Using this data I post-processed the files in project உளி வீரன்.
We are able to look at data from Project Madurai e-Texts. Currently 4,036,616 total words – 40 lakh plus words – in ‘plain_text’ folder which contains unigram data and bigram data at word level. One may use open-tamil library to: – discover the unigram word-frequency of this corpus – discover the bi-gram word-frequency of this corpus (since successive words occur in successive lines).
Using the techniques laid out in earlier blog post on Morse Code we are able to regenerate the Morse Code for Tamil using additional data.
Average code word length = 6.65456 bits. Morse code for Tamil using Madurai corpus is displayed below [most frequently occurring symbols to least] – i.e. in descending order.
We are able to say this Morse code book is a better representation of Tamil since it finds 290 letters incident of the 323 letters in Grantha + Tamil letter set generated from the Madurai corpus of 4 million words.
Can we compose a Tamil Morse-code ? Yes, we can.
International Morse Code – Source: Wikipedia
Tamil Morse Code Table generated from Open-Tamil library. See here for full code and methodology. Full table follows.
Please note table was updated to show letters in most-frequent to least-frequent alphabets and their code-words used. Updated after publishing on Aug 16th, 2018.
Information theory provides us with tools to calculate the information content of symbols in a language, i.e. alphabets in our case. Average codeword length was 6.45652 bits, which is rounded to 7bits.
According to 230+ symbols of encoded in binary without attention to letter frequency we would be using ceil[ log2[230] ] ~ 8bits per symbol, so the usage of Morse code provides a related data compression of 12.5%!
Previously, I had written about Morse code for Tamil in this blog here, and relationship with Unigram, Bigram and Trigram models and word-structure in Tamil language.
Of course 15 of 247 letters are perhaps not received any codeword in this codebook. Further with inclusion of Grantha letters, 323 letters exist in Tamil some of which we don’t have code words.
Further, a large text corpus like Project Madurai’s [PM] unigram frequency distribution maybe useful to develop a widely representative Morse code table. Once you have this PM unigram data, you know how to get this Tamil Morse codebook regenerated!
2007-இல் எனது முதல் தமிழ் மென்பொருளை உருவாக்கினேன். இன்று தொலைந்த மென்பொருள்கலில் ஒன்று. எப்படி தொலயவிட்டேன் ? காலம்.
Tamilpesu.us updated on Aug 8th. This brings all bug fixes of the development in Open-Tamil from March 2018 to present, and new functionality via Tamil text summarizer. This works on text analysis of essay input to split into sentences and words, and forms a correlation matrix to develop a score based off that we pull sentences from text into final summary. Give it a try http://tamilpesu.us/
Text summarizer output of the article from The Hindu Tamil newspaper. Its quite relevant summary in this case, providing an agreeable output.
Unfortunately we are not able to put the Tamil word classifier [using SciKit Learn and Python] online since currently the Python/Django dependency on AWS machine is incompatible; wait for that in future or try it out by yourself.
Thanks to all our contributors, as always to keep this volunteer work going.
