either printed or handwritten

Here are a few use cases for this project:

1. Healthcare Automation: The model can be used to digitize handwritten medical prescriptions thus reducing manual transcription errors and streamlining the process in pharmacies and hospitals.

2. Historical Document Digitization: This model could be utilized for transcribing old handwritten medical documents for research purposes.

3. Handwriting Analysis Tool: The model can be used for general handwriting analysis purposes, for example in educational institutions to improve handwriting recognition or in forensic analysis.

4. OCR Software Improvement: This model can be integrated with OCR (Optical Character Recognition) software to enhance its performance in recognizing and interpreting handwritten text, capitalizing on the diverse range of characters available.

5. Medical Informatics Studies: Researchers using digital health records for epidemiological studies can utilize this model to extract data from handwritten prescriptions or doctor's notes.

Here are a few use cases for this project:

1. Medical Transcription Assistant: This computer vision model can be used in transcription services, helping transcribe handwritten prescriptions into digital text. Doctors, pharmacists, and healthcare professionals can use such transcriptions for digital record-keeping, data analysis, sharing medical information, and patient follow-ups.

2. Medicine Inventory Management: The model can help pharmacies automate their drug inventory management. By identifying medicines names in prescriptions, the software can update inventory data in real time, ensuring that stocks are always updated and adequate.

3. Drug Interaction Analysis: The model can be applied in an application that identifies potential drug interactions for a given patient's multiple prescriptions. By recognizing the names of medicines, it could cross-check them with a database of known drug interactions, alerting the pharmacist or patient about potential risks.

4. Telemedicine Applications: This model can be useful in telemedicine scenarios where patients send images of their prescriptions. It can analyze the prescription, identify the drug names, and forward the information to online pharmacies for home deliveries or to doctors/nurses for tele-consultations.

5. Pharma Market Research: Companies can use this model to analyze prescriptions to understand the most commonly prescribed drugs, aiding in market research and trending analysis in pharmaceutical industries.

chaithanyakota/100-handwritten-medical-records dataset hosted on Hugging Face and contributed by the HF Datasets community

Medical Prescription Handwritten Words

This dataset contains images of individual handwritten medical words extracted from prescription notes. It is designed for training and evaluating handwriting recognition models in the healthcare domain.

  Structure

images/: Contains 40+ handwritten word images (e.g., Amoxicillin.png, Cold.png, Tablet.png, 0.png, etc.) data.csv: Maps each image file to its corresponding label (word)

  Example Use Cases

OCR (Optical… See the full description on the dataset page: https://huggingface.co/datasets/avi-kai/Medical_Prescription_Handwritten_Words.

Here are a few use cases for this project:

1. Medication Transcription: This computer vision model can be used by pharmacies or hospitals to transcribe doctors' handwritten prescriptions into digital form, ensuring that the correct medication and dosage information is captured and understood accurately.

2. Drug Adherence Verification: The model could be employed to verify if patients are taking their prescribed medication on time. It could be used to scan images of medication bottles or packaging, confirming that the correct medication is being taken and identifying any potential medication errors.

3. Pharmaceutical Research: Researchers can use this model to analyze large volumes of prescriptions, helping them understand patterns in medications prescribed for specific diseases. This can lead to valuable insights into treatment outcomes, overprescription, or underprescription practices.

4. Integrated Healthcare Systems: In a more integrated healthcare system, this model could work as part of a comprehensive platform. It could allow the instant uploading of prescriptions into a patient's digital health record, helping doctors to track medication history and potentially flagging drug interactions.

5. Drug Inventory Management: Pharmacies could use the model to automate the process of inventory management by scanning doctors' prescriptions. The system could automatically update stocks of various drugs, helping with demand forecasting and ensuring vital drugs are always available.

1) A total of 3900 medication pack images were captured at different random angles. There are 26 images per pack. 2) Backgrounds are made of paper, cardboard, and plastic. 3) No transparent, reflexive, or patterned backgrounds are used. 4) 300 images were captured while enabling the flashlight. 5) 3600 images were captured in different lighting conditions without using a flashlight. Some images were even captured at dawn time to try the worst lighting conditions. 6) Pharmaceutical packs only were captured. Nothing was captured without its pack. 7) Captured medication packs contain tablets, capsules, syrups, creams, ampoules, gels, drops, ointments, and other types. Two types of special medical tapes were also captured. 8) 150 different pharmaceutical packages were captured. If there is a difference in the dose, size, or type, it is considered a different pack. 9) Some images were captured by different people after receiving the instructions to simulate a real patient experience. 10) Images were captured using 6 devices: • Huawei P30 Lite • Huawei Cun-L21 • Samsung A50 • Samsung A30s • iPhone XS Max • iPhone 11 Pro Max 11) Some medication packs have handwriting. 12) Some images contain shadows and flash burns. 13) 13) Some packs have different types of damage such as having spills, being ripped, etc. 14) Some images are slightly blurred. 15) All captures were taken in all day and night times for several days to try as much lighting and shadow conditions as possible. 16) The images are in the following resolutions: • 3264 x 2448 pixels – 72 dpi • 4000 x 3000 pixels – 72 dpi • 5632 x 4224 pixels – 96 dpi • 4032 x 3024 pixels – 72 dpi • 4032 x 2268 pixels – 72 dpi Images with dimensions that are greater than or equal to 4000 pixels were reduced by 20% for storing purposes.