The A2iA Multi-lingual Text Recognition System at the Second Maurdor Evaluation
Bastien Moysset, Théodore Bluche, Maxime Knibbe, Mohamed Faouzi BenZeghiba, Ronaldo Messina, Jérôme Louradour, Christopher Kermorvant · 2014
This paper describes the system submitted by A2iA to the second Maurdor evaluation for multi-lingual text recognition. A system based on recurrent neural networks and weighted finite state transducers was used both for printed and handwritten recognition, in French, English and Arabic. To cope with the difficulty of the documents, multiple text line segmentations were considered. An automatic procedure was used to prepare annotated text lines needed for the training of the neural network. Language models were used to decode sequences of characters or words for French and English and also sequences of part-of-arabic words (PAWs) in case of Arabic. This system scored first at the second Maurdor evaluation for both printed and handwritten text recognition in French, English and Arabic.