Internship in Deep Learning models for historical handwriting recognition

As for many applications of computer vision, significant progress have been achieved recently in the field of handwriting recognition, thanks to Deep Learning. Handwriting recognition systems are very similar to speech recognition ones: convolutional neural networks are used to extract features, recurrent neural networks model the sequences of characters and attention mechanisms are used to follow the writing and decode the sequence of words. Similarly to other applications of deep learning, the performance are strongly correlated to the quantity of good quality annotated data. However, when working with historical documents, collecting a large quantity of annotated samples from handwritten text may be difficult: transcribing documents in latin or old languages can only be done by paleographers. New training approaching, taking advantage of partially transcribed documents or being able to adapt in an unsupervised ways to a homogeneous collection of document must be developed.

Missions

The workplan is the following :

Environment

Place

TEKLIA, 30 rue Raymond Losserand, 75015 Paris

Contact

Christopher Kermorvant : kermorvant@teklia.com

Bibliography

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