# A Multitask Network for Localization and Recognition of Text in Images

arXiv: https://arxiv.org/abs/1906.09266 · HTML: https://mlsanity.com/paper/1906.09266/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Natural Scene Text Editing Based on AI](https://mlsanity.com/paper/2111.15475/) (2021)

## Abstract (arXiv)

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.