  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.
