# Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application

arXiv: https://arxiv.org/abs/2203.14031 · HTML: https://mlsanity.com/paper/2203.14031/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 26 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification](https://mlsanity.com/paper/2005.14117/) (2020)
- [Deep learning for source camera identification on mobile devices](https://mlsanity.com/paper/1710.01257/) (2017)
- [Deep Temporal Analysis for Non-Acted Body Affect Recognition](https://mlsanity.com/paper/1907.09945/) (2019)
- [Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals](https://mlsanity.com/paper/2203.05864/) (2022)

## Built on by (most cited first, 1 of 1)

- [Augmented Reality in Cultural Heritage: A Dual-Model Pipeline for 3D Artwork Reconstruction](https://mlsanity.com/paper/2507.13719/) (2025)

## Abstract (arXiv)

Taking medicines is a fundamental aspect to cure illnesses. However, studies have shown that it can be hard for patients to remember the correct posology. More aggravating, a wrong dosage generally causes the disease to worsen. Although, all relevant instructions for a medicine are summarized in the corresponding patient information leaflet, the latter is generally difficult to navigate and understand. To address this problem and help patients with their medication, in this paper we introduce an augmented reality mobile application that can present to the user important details on the framed medicine. In particular, the app implements an inference engine based on a deep neural network, i.e., a densenet, fine-tuned to recognize a medicinal from its package. Subsequently, relevant information, such as posology or a simplified leaflet, is overlaid on the camera feed to help a patient when taking a medicine. Extensive experiments to select the best hyperparameters were performed on a dataset specifically collected to address this task; ultimately obtaining up to 91.30% accuracy as well as real-time capabilities.