Multilabel Connectionist Temporal Classification The Connectionist Temporal Classification (CTC) loss function 1 enables endtoend training of a neural network for sequencetosequence tasks without the need for prior alignments between the input and output. CTC is traditionally used for training sequential, singlelabel problems; each element in the sequence has only one class. In this work, we show that CTC is not suitable for multilabel tasks and we present a novel Multilabel Connectionist Temporal Classification (MCTC) loss function for multilabel, sequencetosequence classification. Multilabel classes can represent meaningful attributes of a single element; for example, in Optical Music Recognition (OMR), a music note can have separate duration and pitch attributes. Our approach achieves stateoftheart results on Joint Handwritten Text Recognition and Name Entity Recognition, Asian Character Recognition, and OMR.

Dissecting Multiline Handwriting for Multidimensional Connectionist Classification MultiDimensional Connectionist Classification is a method for weakly supervised training of Deep Neural Networks for segmentationfree multiline offline handwriting recognition. MDCC applies Conditional Random Fields as an alignment function for this task. We discuss the structure and patterns of handwritten text that can be used for building a CRF. Since CRFs are cyclic graphical models, we have to resort to approximate inference when calculating the alignment of multiline text during training, here in the form of Loopy Belief Propagation. This work concludes with experimental results for transcribing small multiline samples from the IAM Offline Handwriting DB which show that MDCC is a competitive methodology.