This paper presents PANTHER, a neural network model for automatic handwriting extraction and recognition in psychodiagnostic questionnaires. Psychodiagnostic tools are essential for assessing and monitoring mental health conditions, but they often rely on pen-and-paper administration, which poses several challenges for data collection and analysis. PANTHER aims to address this problem by using a convolutional neural network to classify scanned questionnaires into their respective types and extract the patient’s responses from the handwritten annotations. The model is trained and evaluated on a dataset of five questionnaires commonly used in psychological and psychiatric settings, achieving high accuracy and similarity scores. The paper also describes the creation of an open-source library based on PANTHER, which can be integrated into a digital platform for delivering psychological services. This paper contributes to the field of computer vision and psychological assessment by providin...

Avis, G., D'Adda, F., Chieregato, D., Guarnieri, E., Meliante, M., Pierotti, A., et al. (2024). A Neural Network for Automatic Handwriting Extraction and Recognition in Psychodiagnostic Questionnaires. In International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings (pp.140-151). Science and Technology Publications, Lda [10.5220/0012587600003699].

A Neural Network for Automatic Handwriting Extraction and Recognition in Psychodiagnostic Questionnaires

Avis G. R.;D'Adda F.;Chieregato D.;Guarnieri E.;Pierotti A. P.;Cremaschi M.
2024

Abstract

This paper presents PANTHER, a neural network model for automatic handwriting extraction and recognition in psychodiagnostic questionnaires. Psychodiagnostic tools are essential for assessing and monitoring mental health conditions, but they often rely on pen-and-paper administration, which poses several challenges for data collection and analysis. PANTHER aims to address this problem by using a convolutional neural network to classify scanned questionnaires into their respective types and extract the patient’s responses from the handwritten annotations. The model is trained and evaluated on a dataset of five questionnaires commonly used in psychological and psychiatric settings, achieving high accuracy and similarity scores. The paper also describes the creation of an open-source library based on PANTHER, which can be integrated into a digital platform for delivering psychological services. This paper contributes to the field of computer vision and psychological assessment by providin...
paper
Computer Vision; Image Classification; Information Extraction; Model Alignment; Questionnaire Digitalisation;
English
10th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2024 - 28 April 2024 through 30 April 2024
2024
International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings
9789897587009
2024
140
151
none
Avis, G., D'Adda, F., Chieregato, D., Guarnieri, E., Meliante, M., Pierotti, A., et al. (2024). A Neural Network for Automatic Handwriting Extraction and Recognition in Psychodiagnostic Questionnaires. In International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings (pp.140-151). Science and Technology Publications, Lda [10.5220/0012587600003699].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/490079
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