  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.
