Q: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Offline handwriting recognition systems require cropped text line images for both<lb>training and recognition. On the one hand, the annotation of position and tran-<lb>script at line level is costly to obtain. On the other hand, automatic line seg-<lb>mentation algorithms are prone to errors, compromising the subsequent recogni-<lb>tion. In this paper, we propose a modification of the popular and efficient multi-<lb>dimensional long short-term memory recurrent neural networks (MDLSTM-<lb>RNNs) to enable end-to-end processing of handwritten paragraphs. More partic-<lb>ularly, we replace the collapse layer transforming the two-dimensional represen-<lb>tation into a sequence of predictions by a recurrent version which can recognize<lb>one line at a time. In the proposed model, a neural network performs a kind of<lb>implicit line segmentation by computing attention weights on the image represen-<lb>tation. The experiments on paragraphs of Rimes and IAM database yield results<lb>that are competitive with those of networks trained at line level, and constitute a<lb>significant step towards end-to-end transcription of full documents.
A:
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition