Learning text-line localization with shared and local regression neural networks

Text line detection and localisation is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents. In this paper, we present a novel approach for text line localisation based on Convolutional Neural Networks and Multidimensional Long Short-Term Memory cells as a regressor in order to predict the coordinates of the text line bounding boxes directly from the pixel values. Targeting typically large images in document image analysis, we propose a new model using weight sharing over local blocks. We compare two strategies: directly predicting the four coordinates or predicting lower-left and upper-right points separately followed by matching. We evaluate our work on the highly unconstrained Maurdor dataset and show that our method outperforms both other machine learning and image processing methods.