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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19683694 |
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| _version_ | 1866901142432120832 |
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| author | Keerthana, A P Kandasamy, Janaki |
| author_facet | Keerthana, A P Kandasamy, Janaki |
| contents | <p>This paper presents a Medical Prescription Readability Checker using Deep Learning to address the challenges posed by illegible handwritten prescriptions in healthcare systems. The proposed system integrates image preprocessing techniques, Optical Character Recognition (OCR), and deep learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for accurate text extraction.<br>The system processes prescription images through noise reduction, thresholding, and feature extraction, followed by sequence modeling to recognize handwritten text. It further employs Named Entity Recognition (NER) and fuzzy matching techniques for extracting and validating critical information such as patient details and medication names.<br>The proposed approach improves prescription readability, reduces the risk of medical errors, and enables efficient digitization of healthcare records. This work highlights the potential of AI-based solutions in enhancing patient safety and supporting modern healthcare systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19683694 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Medical Prescription Analysis Using Deep Learning for Dosage and Timing Recommendations Keerthana, A P Kandasamy, Janaki <p>This paper presents a Medical Prescription Readability Checker using Deep Learning to address the challenges posed by illegible handwritten prescriptions in healthcare systems. The proposed system integrates image preprocessing techniques, Optical Character Recognition (OCR), and deep learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for accurate text extraction.<br>The system processes prescription images through noise reduction, thresholding, and feature extraction, followed by sequence modeling to recognize handwritten text. It further employs Named Entity Recognition (NER) and fuzzy matching techniques for extracting and validating critical information such as patient details and medication names.<br>The proposed approach improves prescription readability, reduces the risk of medical errors, and enables efficient digitization of healthcare records. This work highlights the potential of AI-based solutions in enhancing patient safety and supporting modern healthcare systems.</p> |
| title | Medical Prescription Analysis Using Deep Learning for Dosage and Timing Recommendations |
| url | https://doi.org/10.5281/zenodo.19683694 |