  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 details
by enhancing factors related to character categories in the reconstruction
process, so the textual information can be more effectively detected and
recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed
lensless images. By performing experiments on datasets of different
complexities, the applicability to text detection and recognition on lensless
cameras was verified. This study reasonably demonstrates text detection and
recognition tasks in the lensless camera system,and develops a basic method for
novel applications.
