Cleaning for Poems: Difference between revisions
From Algolit
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by Algolit | by Algolit | ||
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| + | [https://gitlab.constantvzw.org/algolit/mundaneum/tree/master/exhibition/3-Cleaners/the-cleaner Sources on Gitlab] | ||
| − | For this exhibition we | + | For this exhibition we worked with 3 per cent of the Mundaneum's archive. These documents were first scanned or photographed. To make the documents searchable they were transformed into text using Optical Character Recognition software (OCR). OCR are algorithmic models that are trained on other texts. They have learned to identify characters, words, sentences and paragraphs. The software often makes 'mistakes'. It might recognize a wrong character, it might get confused by a stain an unusual font or the reverse side of the page being visible. |
| − | The software | + | |
| − | + | While these mistakes are often considered noise, confusing the training, they can also be seen as poetic interpretations of the algorithm. They show us the limits of the machine. And they also reveal how the algorithm might work, what material it has seen in training and what is new. They say something about the standards of its makers. In this installation we ask your help in verifying our dataset. As a reward we'll present you with a personal algorithmic improvisation. | |
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Latest revision as of 17:52, 4 June 2019
by Algolit
For this exhibition we worked with 3 per cent of the Mundaneum's archive. These documents were first scanned or photographed. To make the documents searchable they were transformed into text using Optical Character Recognition software (OCR). OCR are algorithmic models that are trained on other texts. They have learned to identify characters, words, sentences and paragraphs. The software often makes 'mistakes'. It might recognize a wrong character, it might get confused by a stain an unusual font or the reverse side of the page being visible.
While these mistakes are often considered noise, confusing the training, they can also be seen as poetic interpretations of the algorithm. They show us the limits of the machine. And they also reveal how the algorithm might work, what material it has seen in training and what is new. They say something about the standards of its makers. In this installation we ask your help in verifying our dataset. As a reward we'll present you with a personal algorithmic improvisation.
Concept, code, interface: Gijs de Heij