# Text-Attentional Convolutional Neural Networks for Scene Text Detection

arXiv: https://arxiv.org/abs/1510.03283 · HTML: https://mlsanity.com/paper/1510.03283/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2015.

## Built on by (most cited first, 12 of 12)

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- [Scene Text recognition with Full Normalization](https://mlsanity.com/paper/2109.01034/) (2021)
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- [Leveraging machine learning for less developed languages: Progress on Urdu text detection](https://mlsanity.com/paper/2209.14022/) (2022)
- [SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization](https://mlsanity.com/paper/2303.07153/) (2023)
- [How can Deep Learning Retrieve the Write-Missing Additional Diagnosis from Chinese Electronic Medical Record For DRG](https://mlsanity.com/paper/2303.16757/) (2023)
- [Research on Multilingual Natural Scene Text Detection Algorithm](https://mlsanity.com/paper/2312.11153/) (2023)
- [Operationalization of Machine Learning with Serverless Architecture: An Industrial Operationalization of Machine Learning with Serverless Architecture: An Industrial Implementation for Harmonized System Code Prediction](https://mlsanity.com/paper/2602.17102/) (2026)

## Abstract (arXiv)

Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.