Text detection and recognition based on a lensless imaging system
Yinger Zhang, Zhouyi Wu, Peiying Lin, Yuting Wu, Lusong Wei, Zhengjie, Huang, and Jiangtao Huangfu

TL;DR
This paper presents a deep-learning pipeline combining lensless imaging, text detection, and recognition models to improve the clarity and accuracy of text recognition from lensless camera data.
Contribution
It introduces a novel framework integrating U-Net, CTPN, and CRNN models for enhanced text detection and recognition in lensless imaging systems.
Findings
Enhanced image clarity with fewer artifacts
Improved accuracy in text detection and recognition
Demonstrated applicability on complex datasets
Abstract
Lensless cameras are characterized by several advantages (e.g., miniaturization, ease of manufacture, and low cost) as compared with conventional cameras. However, they have not been extensively employed due to their poor image clarity and low image resolution, especially for tasks that have high requirements on image quality and details such as text detection and text recognition. To address the problem, a framework of deep-learning-based pipeline structure was built to recognize text with three steps from raw data captured by employing lensless cameras. This pipeline structure consisted of the lensless imaging model U-Net, the text detection model connectionist text proposal network (CTPN), and the text recognition model convolutional recurrent neural network (CRNN). Compared with the method focusing only on image reconstruction, UNet in the pipeline was able to supplement the imaging…
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Taxonomy
TopicsRandom lasers and scattering media · Digital Media Forensic Detection · Advanced Optical Imaging Technologies
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Concatenated Skip Connection · Max Pooling · Convolution · U-Net
