Description

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 convolutional - recurrent architectures (CNN-RNN) to recognize text in Spanish printed sources from the seventeenth century.

Duration

Total project length: 175 hours

Task ideas

• Creation of an hybrid end-to-end model based on convolutional - recurrent architectures (CNN-RNN) capable of performing text recognition

Expected results

• Machine learning models will be trained to perform text recognition of non-standard printed text
• AI should be able to extract text with at least 80% accuracy

Requirements

Python and some previous experience in Machine Learning.

Difficulty level

Medium

Test

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

Mentors

• Sergei Gleyzer (University of Alabama)
• Xabier Granja (University of Alabama)
• Emanuele Usai (University of Alabama)

Please DO NOT contact mentors directly by email. Instead, please email human-ai@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.

Corresponding Project

• RenAIssance

Participating Organizations

• Alabama
• Delhi

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