  Telemedicine and mobile health applications, especially during the quarantine
imposed by the covid-19 pandemic, led to an increase on the need of
transferring health monitor readings from patients to specialists. Considering
that most home medical devices use seven-segment displays, an automatic display
reading algorithm should provide a more reliable tool for remote health care.
This work proposes an end-to-end method for detection and reading seven-segment
displays from medical devices based on deep learning object detection models.
Two state of the art model families, EfficientDet and EfficientDet-lite,
previously trained with the MS-COCO dataset, were fine-tuned on a dataset
comprised by medical devices photos taken with mobile digital cameras, to
simulate real case applications. Evaluation of the trained model show high
efficiency, where all models achieved more than 98% of detection precision and
more than 98% classification accuracy, with model EfficientDet-lite1 showing
100% detection precision and 100% correct digit classification for a test set
of 104 images and 438 digits.
