Expiry date recognition using deep neural networks

This paper proposes a deep learning solution for optical character recognition, specifically tuned to detect expiration dates that are printed on the packaging of food items. This method can be used to reduce food waste, having a significant impact on the design of smart refrigerators and can prove especially useful for persons with vision difficulties, by combining it with a speech synthesis engine. The main problem in designing an efficient solution for expiry date recognition is the lack of a large enough dataset to train deep neural networks. To tackle this issue, we propose to use an additional dataset composed of synthetically generated images. Both the synthetic and real image datasets are detailed in the paper and we show that the proposed method offers a 9.4% accuracy improvement over using real images alone.

Mobile application to improve reading habits using Virtual Reality

Education is changing rapidly, so emerging technologies are being used to improve this process. One of these technologies is the Virtual Reality (VR), whose field of action is increasingly broad, so it has been incorporating new methods in teaching having a great positive impact in recent years, but the applicability in the area of communication is minimal. Seeing the challenges faced by the Ministry of Education in the development of reading skills and strengthening the capabilities of students in our country. In the this research a new alternative is proposed, to improve the beginnings of the habit of reading in students of second grade of Elementary School having to use of a mobile application with VR named Diverticuentos that generates scenes of the readings in 360o besides being con-nected to a databases like firebase that allows us to see the progress of each stu-dent verifying that it is possible to integrate this new technology in the sessions and to generate a beginning of habit of reading of the students.

NLP based Deep Learning Approach for Plagiarism Detection

Plagiarism detection represents an application domain for the NLP research area, which has not been investigated too much by researchers in the context of lately developed attention mechanism and sentence transformers. In this paper, we present a plagiarism detection approach which uses state-of-the-art deep learning techniques in order to provide more accurate results than classical plagiarism detection techniques. This approach goes beyond classical word searching and matching, which is time-consuming and can be easily cheated because it uses attention mechanisms and aims for text encoding and contextualization. In order to get proper insight regarding the system, we investigate three approaches in order to be sure that the results are relevant and well-validated. The experimental results show that the systems that use BERT pre-trained model offers the best results and outperforms GloVe and RoBERTa