Text Recognition with Transformer Models


Transliteration of text from centuries-old works represents a research area that is underserved by current tools, such as Adobe Acrobat’s OCR. While these resources can perform text recognition from clearly printed modern sources, they are incapable of extracting textual data from early forms of print, much less manuscripts. This project will focus on the application of hybrid end-to-end models based on transformers (e.g. VIT-RNN or CNN-TF or VIT-TF) to recognize text in Spanish printed sources from the seventeenth century.


Total project length: 175 hours

Task ideas

Expected results


Python and some previous experience in Machine Learning.

Difficulty level



Please use this link to access the test for this project.


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