Automated medical labels detection and text extraction / Abdelbasset Benothmani

Public ISBD Titre : Automated medical labels detection and text extraction Type de document : document multimédia Auteurs : Abdelbasset Benothmani, Auteur ; Ikhlas Djoudi, Auteur ; Mustapha Bouakkaz, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2025 Importance : 85 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Information and decision systems - Distributed networks, systems, and applications Langues : Anglais Mots-clés : Deep Learning  Handwriting Recognition  Medical prescriptions/Prescriptions  Artificial intelligence (AI)  Healthcare  Neural Networks  Vision Transformers  Hybrid Models (CNN- RNN) Résumé : Modern technology aims to automatically read and understand handwritten prescriptions using deep learning. This is critical given that handwritten notes are often illegible and difficult to read, which can lead to serious medical errors. Deep learning, a branch of artificial intelligence, is used to train computer models to accurately recognize and interpret these prescriptions. Using large amounts of data and powerful algorithms, deep learning models can learn to identify handwriting patterns and extract important information such as medication names and dosages. This technology contributes to improving the safety and efficiency of healthcare services, as it can significantly reduce documentation errors caused by illegible handwriting, ensuring that clinical decisions are based on accurate and timely information. In this note, we address the future role of deep learning in this field, noting that new neural network architectures such as vision transformers and hybrid CNN-RNN models promise to increase the accuracy and effectiveness of handwritten note recognition. It explains that AI-based handwriting recognition in electronic health records (EHRs) can speed up communication between teams and reduce administrative burdens, allowing clinicians to focus on direct patient care rather than burdensome paperwork, thereby reducing burnout and increasing job satisfaction. In conclusion, we conclude that the revolutionary potential of deep learning in medical handwriting recognition has the potential to revolutionize health outcomes by ensuring accurate, timely, and efficient recording. It suggests that adopting these innovations will be pivotal in developing a healthcare system where technology enables but does not replace human empathy and clinical expertise. note de thèses : Mémoire de master en informatique Automated medical labels detection and text extraction [document multimédia] / Abdelbasset Benothmani, Auteur ; Ikhlas Djoudi, Auteur ; Mustapha Bouakkaz, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 85 p. + 1 disque optique numérique (CD-ROM). Option : Information and decision systems - Distributed networks, systems, and applications Langues : Anglais Mots-clés : Deep Learning  Handwriting Recognition  Medical prescriptions/Prescriptions  Artificial intelligence (AI)  Healthcare  Neural Networks  Vision Transformers  Hybrid Models (CNN- RNN) Résumé : Modern technology aims to automatically read and understand handwritten prescriptions using deep learning. This is critical given that handwritten notes are often illegible and difficult to read, which can lead to serious medical errors. Deep learning, a branch of artificial intelligence, is used to train computer models to accurately recognize and interpret these prescriptions. Using large amounts of data and powerful algorithms, deep learning models can learn to identify handwriting patterns and extract important information such as medication names and dosages. This technology contributes to improving the safety and efficiency of healthcare services, as it can significantly reduce documentation errors caused by illegible handwriting, ensuring that clinical decisions are based on accurate and timely information. In this note, we address the future role of deep learning in this field, noting that new neural network architectures such as vision transformers and hybrid CNN-RNN models promise to increase the accuracy and effectiveness of handwritten note recognition. It explains that AI-based handwriting recognition in electronic health records (EHRs) can speed up communication between teams and reduce administrative burdens, allowing clinicians to focus on direct patient care rather than burdensome paperwork, thereby reducing burnout and increasing job satisfaction. In conclusion, we conclude that the revolutionary potential of deep learning in medical handwriting recognition has the potential to revolutionize health outcomes by ensuring accurate, timely, and efficient recording. It suggests that adopting these innovations will be pivotal in developing a healthcare system where technology enables but does not replace human empathy and clinical expertise. note de thèses : Mémoire de master en informatique

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