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Offline handwriting recognition systems require cropped text line images forboth training and recognition. On the one hand, the annotation of position andtranscript at line level is costly to obtain. On the other hand, automatic linesegmentation algorithms are prone to errors, compromising the subsequentrecognition. In this paper, we propose a modification of the popular andefficient multi-dimensional long short-term memory recurrent neural networks(MDLSTM-RNNs) to enable end-to-end processing of handwritten paragraphs. Moreparticularly, we replace the collapse layer transforming the two-dimensionalrepresentation into a sequence of predictions by a recurrent version which canrecognize one line at a time. In the proposed model, a neural network performsa kind of implicit line segmentation by computing attention weights on theimage representation. The experiments on paragraphs of Rimes and IAM databaseyield results that are competitive with those of networks trained at linelevel, and constitute a significant step towards end-to-end transcription offull documents.

Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition