# Image-free multi-character recognition

arXiv: https://arxiv.org/abs/2112.10587 · HTML: https://mlsanity.com/paper/2112.10587/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Scene Text Detection and Recognition: The Deep Learning Era](https://mlsanity.com/paper/1811.04256/) (2018)
- [Benchmark for License Plate Character Segmentation](https://mlsanity.com/paper/1607.02937/) (2016)
- [LPRNet: License Plate Recognition via Deep Neural Networks](https://mlsanity.com/paper/1806.10447/) (2018)
- [SEE: Towards Semi-Supervised End-to-End Scene Text Recognition](https://mlsanity.com/paper/1712.05404/) (2017)
- [Experimental comparison of single-pixel imaging algorithms](https://mlsanity.com/paper/1707.03164/) (2017)
- [SAR image matching algorithm based on multi-class features](https://mlsanity.com/paper/2108.06009/) (2021)

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

- [Solving combinational optimization problems with evolutionary single-pixel imaging](https://mlsanity.com/paper/2210.05923/) (2022)

## Abstract (arXiv)

The recently developed image-free sensing technique maintains the advantages of both the light hardware and software, which has been applied in simple target classification and motion tracking. In practical applications, however, there usually exist multiple targets in the field of view, where existing trials fail to produce multi-semantic information. In this letter, we report a novel image-free sensing technique to tackle the multi-target recognition challenge for the first time. Different from the convolutional layer stack of image-free single-pixel networks, the reported CRNN network utilities the bidirectional LSTM architecture to predict the distribution of multiple characters simultaneously. The framework enables to capture the long-range dependencies, providing a high recognition accuracy of multiple characters. We demonstrated the technique's effectiveness in license plate detection, which achieved 87.60% recognition accuracy at a 5% sampling rate with a higher than 100 FPS refresh rate.