# STN-OCR: A single Neural Network for Text Detection and Text Recognition

arXiv: https://arxiv.org/abs/1707.08831 · HTML: https://mlsanity.com/paper/1707.08831/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

## Built on by (most cited first, 3 of 3)

- [Scene Text Recognition with Semantics](https://mlsanity.com/paper/2210.10836/) (2022)
- [An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text](https://mlsanity.com/paper/2304.08670/) (2023)
- [Character Recognition in Byzantine Seals with Deep Neural Networks](https://mlsanity.com/paper/2401.10741/) (2024)

## Abstract (arXiv)

Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In re- cent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present STN-OCR, a step towards semi-supervised neural networks for scene text recognition, that can be optimized end-to-end. In contrast to most existing works that consist of multiple deep neural networks and several pre-processing steps we propose to use a single deep neural network that learns to detect and recognize text from natural images in a semi-supervised way. STN-OCR is a network that integrates and jointly learns a spatial transformer network, that can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We investigate how our model behaves on a range of different tasks (detection and recognition of characters, and lines of text). Experimental results on public benchmark datasets show the ability of our model to handle a variety of different tasks, without substantial changes in its overall network structure.