We present an end-to-end trainable multi-task network that addresses the problem of lexicon-free text extraction from complex documents. This network simultaneously solves the problems of text localization and text recognition and text segments are identified with no post-processing, cropping, or word grouping. A convolutional backbone and Feature Pyramid Network are combined to provide a shared representation that benefits each of three model heads: text localization, classification, and text recognition. To improve recognition accuracy, we describe a dynamic pooling mechanism that retains high-resolution information across all RoIs. For text recognition, we propose a convolutional mechanism with attention which out-performs more common recurrent architectures. Our model is evaluated against benchmark datasets and comparable methods and achieves high performance in challenging regimes of non-traditional OCR.
@article{arxiv.1906.09266,
title = {A Multitask Network for Localization and Recognition of Text in Images},
author = {Mohammad Reza Sarshogh and Keegan E. Hines},
journal= {arXiv preprint arXiv:1906.09266},
year = {2019}
}