# Patch Aggregator for Scene Text Script Identification

arXiv: https://arxiv.org/abs/1912.03818 · HTML: https://mlsanity.com/paper/1912.03818/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

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

- [LISTER: Neighbor Decoding for Length-Insensitive Scene Text Recognition](https://mlsanity.com/paper/2308.12774/) (2023)
- [TeLCoS: OnDevice Text Localization with Clustering of Script](https://mlsanity.com/paper/2104.08045/) (2021)
- [On-Device Spatial Attention based Sequence Learning Approach for Scene Text Script Identification](https://mlsanity.com/paper/2112.00448/) (2021)

## Abstract (arXiv)

Script identification in the wild is of great importance in a multi-lingual robust-reading system. The scripts deriving from the same language family share a large set of characters, which makes script identification a fine-grained classification problem. Most existing methods make efforts to learn a single representation that combines the local features by making a weighted average or other clustering methods, which may reduce the discriminatory power of some important parts in each script for the interference of redundant features. In this paper, we present a novel module named Patch Aggregator (PA), which learns a more discriminative representation for script identification by taking into account the prediction scores of local patches. Specifically, we design a CNN-based method consisting of a standard CNN classifier and a PA module. Experiments demonstrate that the proposed PA module brings significant performance improvements over the baseline CNN model, achieving the state-of-the-art results on three benchmark datasets for script identification: SIW-13, CVSI 2015 and RRC-MLT 2017.