a picture is worth a thousand words

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Let there be House

House-ENG

Download house-eng.pdf (234kB).

Download house-esp.pdf (264kB).

House MD is one of my favorites TV show, I really enjoy the show. House deserves a tribute!.

Hands on it, I collected all the subtitles of the first five seasons from the website http://www.tvsubtitles.net/ something like 115 episodes.

Because the subtitles are ‘common language’ there is not so much information as in a song or a speech. I needed to clean up the word list in order to have a significant result.

So I increased the numbers of words that the algoritm will discard during the counting process
using the 1000 most commonly used English words that I found in http://www.duboislc.org/EducationWatch/First100Words.html

I also realized that there is a lot of strange words with only one hit, probably due to the nature of the subtitles. I decided to include only the words with two or more hits.

Here is the final image!
 Cuddy would be proud.

As usual, all files for personal use only.

New York City

NewYorkCity-ENG

Download newyorkcity-eng.pdf (740kB).

Words taken from wikipedia article about New York City. Original image from the same wikipedia article. It’s a panoramic night view, looking north from the Empire State Building (image from Diliff)

This is an oportunity to test the limits of the algorithm. So I’m thinking of a really large print, like a building facade or the interior wall of a restaurant (of course I’m architect). This will be the target, don’t blame me if you try to print it on a little piece of paper.

So there will be new rules: Only uppercase or firstcase words, these must be representative words of the city. Only words with two or more hits, I only  need the most representative of them.  Result
 there is only 223 words left from (aprox) 3.000.

First twenty words: york 250, manhattan 52, american 48, brooklyn 23, bronx 22, queens 19, staten 18,central 15, jersey 11, lower 11, university 11, county 10, international 10, hall 9, boroughs 8, court 8, liberty 8, african 7, broadway 7, college 7.

After a couple of test I realised that I cannot use the count method to calculate the word size, because I need to put almost 20.000 words to get a nice result. Then I discarded the counter value and redistributed the sizes using an exponential function between two fixed values.

Hope you like it. Images free for personal use.

Goodbye Michael


MichaelJackson-ENGDownload Michael Jackson (English version).pdf (190kB).

Download Michael Jackson (Spanish version).pdf (188kB).

This is an updated version of my ‘goodbye message’ for Michael Jackson. This time I collected more than four thousand messages that people posted in http://www.lacortedelreydelpop.com/ after the sudden death of Michael.

All the messages were written in Spanish so, in this case, the English version is a direct translation of the spanish using Google.

I did not discrimínate. The image contains all the words of all the messages
 even the ‘not so nice’ ones.

20 first words:michael 3158, always 2734, music 1509, king 1423, jackson 1173, pop 1164, heart 1158, life 1023, thanks 919, never 870, world 869, peace 820, best 40, god 725, left 723, big 716, dead 664, rests 656, believe 651, alone 648.

My apologies because I cannot find the website that hosted the original image. It was a very nice abstract image of an asian artist
. I think.

Those images are free for non-commercial personal use.

Play it again, Eddie.

IronMaiden ENGSame approach as the Obama post.

Download Iron Maiden (English version).pdf (195kB).
Download Iron Maiden (Spanish version).pdf (174kB).

In this case I used more than one hundred and fifty lyrics from Iron Maiden songs over an image of Eddie The Head, the well know band mascot (one of these incredibles Derek Riggs illustrations). The font used is Metal Lord, based in the band logo.

As usual you can use these files only for personal use, and you cannot modify them.

First 20 (of 2088) more often words in the lyrics: just 175, time 169, know 165, see 143, come 138, live 137, now 137, can 133, feel 127, get 114, take 113, dream 110, look 105, got 103, tell 103, go 101, life 99, world 97, make 95, like 89.

Other links:

Derek Riggs homepage.
Iron Maiden homepage.

(a tribute to ‘Rafa the Head’)

Obama Speeches

Click in the image for high resolution. You can also download the PDF English version or the Spanish one. In the PDF version the words are vectors so you can print it in a very large format (ISO-A1 or so) to have a nice poster for your office/room.

Download Obama Speeches (English version).pdf (360kB).

Download Obama Speeches (Spanish version).pdf (360kB).

All those images are free for non-commercial personal use. You can distribute freely, but you cannot modify them in any way. Please refer to this site if you redistribute them.

Inspired by the excellent work of Jeff Clark (http://neoformix.com/) I coded an algorithm in Processing that transform images in words in different ways.

I have tested it for a few months and now I want to share the results with you. This is the first example of a series of texts art images that Iwill be posting in the next weeks. Here is the process


First I took the transcriptions of more than one hundred Obama speeches from the website http://obamaspeeches.com/. Then I counted all words using an adapted version of the Snowball stemmer for Processing (thanks to Lot!, http://feenelcaos.org/). I also picked up an Obama photo using Google Image to find a good one.

Finally I use my ‘image pattern searcher’ algorithm (I have to find a name for it) to fit all the words in the image. In this case the more times the word appears in the speeches the bigger it is (proportionally).

Here you have an example of the 20 more often words in the speech:  can,785, american,683 , people,648, one,619, time,561, years,548, country,521, work,481, make,477, need,454, just,440, know,415, america,399, go,384, come,356, care,355, nation,352, now,345, like,343, right,332.

In order to get a nicer result I also let the algorithm reuse words but only in a smaller font size.

Note that the Spanish version of this image used a direct Google translation of the English words. So some of them may sound like “Spanglish” or have a bad translation because Google cannot take care of the context.


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