Muoro secures a $3.2M grant from Brownfield to expand Global Capability Centers and Centres of Excellence in tier-II cities, North India.•Value Engineering Partner for AI, Data & Modernization•Engineered, Operated and owned within explicit controlled boundaries•
Muoro secures a $3.2M grant from Brownfield to expand Global Capability Centers and Centres of Excellence in tier-II cities, North India.•Value Engineering Partner for AI, Data & Modernization•Engineered, Operated and owned within explicit controlled boundaries•
Muoro secures a $3.2M grant from Brownfield to expand Global Capability Centers and Centres of Excellence in tier-II cities, North India.•Value Engineering Partner for AI, Data & Modernization•Engineered, Operated and owned within explicit controlled boundaries•
Indian Healthcare Prescription OCR
Muoro built a hybrid CNN-LSTM vision model to digitise handwritten prescriptions for an Indian healthcare provider, turning scans into structured, EHR-ready data.
Business Outcomes
Business Outcomes
High Accuracy
The model achieved high accuracy on handwritten medical text.
Less Manual Verification
Manual verification load was reduced for pharmacists.
EHR-Deployable
The API is deployable directly into EHR and telemedicine platforms.About Client
The client is an Indian healthcare provider integrating prescription digitisation into its EHR and telemedicine platforms. Handwritten prescriptions from many different doctors, in varied formats and abbreviations, previously required manual re-entry by pharmacists before dispensing could proceed.Healthcare
The Challenge
Handwriting styles and cursive script varied widely across doctors.
The Challenge
Handwriting styles and cursive script varied widely across doctors.
Noisy Scans
Scans carried blur, skew, lighting issues, and other artefacts.Complex Layouts
Layouts mixed patient, drug, dose, and instruction fields inconsistently.Unreliable Off-the-Shelf OCR
Standard OCR tools proved unreliable on medical text.Noisy Scans
Scans carried blur, skew, lighting issues, and other artefacts.Complex Layouts
Layouts mixed patient, drug, dose, and instruction fields inconsistently.Unreliable Off-the-Shelf OCR
Standard OCR tools proved unreliable on medical text.Noisy Scans
Scans carried blur, skew, lighting issues, and other artefacts.Complex Layouts
Layouts mixed patient, drug, dose, and instruction fields inconsistently.Unreliable Off-the-Shelf OCR
Standard OCR tools proved unreliable on medical text.Noisy Scans
Scans carried blur, skew, lighting issues, and other artefacts.Complex Layouts
Layouts mixed patient, drug, dose, and instruction fields inconsistently.Unreliable Off-the-Shelf OCR
Standard OCR tools proved unreliable on medical text.The Muoro Solution
CNN feature extraction was combined with LSTM and an attention mechanism.
The Muoro Solution
CNN feature extraction was combined with LSTM and an attention mechanism.
Hybrid Vision Model
CNN feature extraction was combined with LSTM and an attention mechanism.Preprocessing Pipeline
An OpenCV and PyTesseract pipeline handled preprocessing end to end.Image Cleanup
Binarisation, deskew, denoise, and morphological filters improved input quality.CNN + LSTM + Attention
|OpenCV
|PyTesseract
|Django API
Impact & Results
The model achieved high accuracy on handwritten medical text.
Impact & Results
The model achieved high accuracy on handwritten medical text.
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Less Manual Verification
Manual verification load was reduced for pharmacists.EHR-Deployable
The API is deployable directly into EHR and telemedicine platforms.Extensible Foundation
The system forms a foundation for multi-language and drug-ontology validation.Less Manual Verification
Manual verification load was reduced for pharmacists.EHR-Deployable
The API is deployable directly into EHR and telemedicine platforms.Extensible Foundation
The system forms a foundation for multi-language and drug-ontology validation.Less Manual Verification
Manual verification load was reduced for pharmacists.EHR-Deployable
The API is deployable directly into EHR and telemedicine platforms.Extensible Foundation
The system forms a foundation for multi-language and drug-ontology validation.Final Outcome
Doctors' handwriting, digitised. Structured prescription data from scan to EHR.
Final Outcome
Doctors' handwriting, digitised. Structured prescription data from scan to EHR.
Turn handwritten medical records into structured data
Use AI-powered computer vision to digitize prescriptions and clinical documents, reduce manual effort, and integrate accurate data into your healthcare systems.
Turn handwritten medical records into structured data
Use AI-powered computer vision to digitize prescriptions and clinical documents, reduce manual effort, and integrate accurate data into your healthcare systems.
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