  There are many real-life use cases such as barcode scanning or billboard
reading where people need to detect objects and read the object contents.
Commonly existing methods are first trying to localize object regions, then
determine layout and lastly classify content units. However, for simple fixed
structured objects like license plates, this approach becomes overkill and
lengthy to run. This work aims to solve this detect-and-read problem in a
lightweight way by integrating multi-digit recognition into a one-stage object
detection model. Our unified method not only eliminates the duplication in
feature extraction (one for localizing, one again for classifying) but also
provides useful contextual information around object regions for
classification. Additionally, our choice of backbones and modifications in
architecture, loss function, data augmentation and training make the method
robust, efficient and speedy. Secondly, we made a public benchmark dataset of
diverse real-life 1D barcodes for a reliable evaluation, which we collected,
annotated and checked carefully. Eventually, experimental results prove the
method's efficiency on the barcode problem by outperforming industrial tools in
both detecting and decoding rates with a real-time fps at a VGA-similar
resolution. It also did a great job expectedly on the license-plate recognition
task (on the AOLP dataset) by outperforming the current state-of-the-art method
significantly in terms of recognition rate and inference time.
