Definition: 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.
Input: 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.

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