# Multilingual Scene Character Recognition System using Sparse Auto-Encoder for Efficient Local Features Representation in Bag of Features

arXiv: https://arxiv.org/abs/1806.07374 · HTML: https://mlsanity.com/paper/1806.07374/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

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

- [Augmented Transformers with Adaptive n-grams Embedding for Multilingual Scene Text Recognition](https://mlsanity.com/paper/2302.14261/) (2023)

## Abstract (arXiv)

The recognition of texts existing in camera-captured images has become an important issue for a great deal of research during the past few decades. This give birth to Scene Character Recognition (SCR) which is an important step in scene text recognition pipeline. In this paper, we extended the Bag of Features (BoF)-based model using deep learning for representing features for accurate SCR of different languages. In the features coding step, a deep Sparse Auto-encoder (SAE)-based strategy was applied to enhance the representative and discriminative abilities of image features. This deep learning architecture provides more efficient features representation and therefore a better recognition accuracy. Our system was evaluated extensively on all the scene character datasets of five different languages. The experimental results proved the efficiency of our system for a multilingual SCR.