# FaSTExt: Fast and Small Text Extractor

arXiv: https://arxiv.org/abs/1908.08994 · HTML: https://mlsanity.com/paper/1908.08994/

## Key facts

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

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

- [Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing](https://mlsanity.com/paper/2303.07525/) (2023)

## Abstract (arXiv)

Text detection in natural images is a challenging but necessary task for many applications. Existing approaches utilize large deep convolutional neural networks making it difficult to use them in real-world tasks. We propose a small yet relatively precise text extraction method. The basic component of it is a convolutional neural network which works in a fully-convolutional manner and produces results at multiple scales. Each scale output predicts whether a pixel is a part of some word, its geometry, and its relation to neighbors at the same scale and between scales. The key factor of reducing the complexity of the model was the utilization of depthwise separable convolution, linear bottlenecks, and inverted residuals. Experiments on public datasets show that the proposed network can effectively detect text while keeping the number of parameters in the range of 1.58 to 10.59 million in different configurations.