# Improving patch-based scene text script identification with ensembles of conjoined networks

arXiv: https://arxiv.org/abs/1602.07480 · HTML: https://mlsanity.com/paper/1602.07480/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2016.

## Built on by (most cited first, 6 of 6)

- [A Multiplexed Network for End-to-End, Multilingual OCR](https://mlsanity.com/paper/2103.15992/) (2021)
- [MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition](https://mlsanity.com/paper/2305.14758/) (2023)
- [Exploring Knowledge Distillation of a Deep Neural Network for Multi-Script identification](https://mlsanity.com/paper/2102.10335/) (2021)
- [TeLCoS: OnDevice Text Localization with Clustering of Script](https://mlsanity.com/paper/2104.08045/) (2021)
- [Separate Scene Text Detector for Unseen Scripts is Not All You Need](https://mlsanity.com/paper/2307.15991/) (2023)
- [MDIW-13: a New Multi-Lingual and Multi-Script Database and Benchmark for Script Identification](https://mlsanity.com/paper/2405.18924/) (2024)

## Abstract (arXiv)

This paper focuses on the problem of script identification in scene text images. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed aspect ratio as in the typical use of holistic CNN classifiers, we propose here a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme. Our experiments with this learning procedure demonstrate state-of-the-art results in two public script identification datasets. In addition, we propose a new public benchmark dataset for the evaluation of multi-lingual scene text end-to-end reading systems. Experiments done in this dataset demonstrate the key role of script identification in a complete end-to-end system that combines our script identification method with a previously published text detector and an off-the-shelf OCR engine.