No abstract

GenericiQ in structured document recognition is a difJicult challenge. We propose in this paper a new generic document recognition method (DMOS) niude of a new gramrnutical formalism (EPF) and an associated parser able to introduce context in segmentation.We implement this method to obtain a generator of document recognition systems. This generator can automatically produce new recognition systems. It is just necessary to describe the document with an EPF grammar which is then siniply compiled. In this way we have developed vurious recognition systems: one on musicat scores, one on mathematical fornzulae and one on recursive table structures. We have ulso dejined a specijic application on quite darnaged military forms of the 19th century.We have been able to test the generated system on 5,000 of these militaty forms. This has permit 11s to validate the DMOS method on a real-world application.

Optical Music Recognition (OMR) is the challenge of understanding the content of musical scores. Accurate detection of individual music objects is a critical step in processing musical documents, because a failure at this stage corrupts any further processing. So far, all proposed methods were either limited to typeset music scores or were built to detect only a subset of the available classes of music symbols. In this work, we propose an end-to-end trainable object detector for music symbols that is capable of detecting almost the full vocabulary of modern music notation in handwritten music scores. By training deep convolutional neural networks on the recently released MUSCIMA++ dataset which has symbol-level annotations, we show that a machine learning approach can be used to accurately detect music objects with a mean average precision of up to 80%.