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              <p>In this work, we study how to extend the capability of BLSTM networks to processdata which are not only text strings but graphical two-dimensional languages such as handwrit-ten mathematical expressions. The proposed solution aims at transforming the mathematicalexpression description into a sequence including at the same time symbol labels and relation-ship labels, so that classical supervised sequence labeling with recurrent neural networks canbe applied. For simple one-dimensional (1-D) expression, we use the Right label to segmentone symbol from the next one, as with the standard blank label for regular text. For genuine two-dimensional (2-D) expressions, we introduce additional speciﬁc labels assigned to each of thedifferent possible spatial relationships that exist between sub-expressions. As a result, BLSTM network is able to perform at the same time the symbol recognition task and the segmentationtask, which is a new perspective for the mathematical expression domain.</p>
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