AI-Powered Cloud Inference Pipeline Development
Budget: ₹12,500 – ₹37,500 INR
Project Title:
Cloud-Based AI Pipeline (YOLO + OCR) for Extracting Data from Dynamic Screens via Image Input
Description:
We are developing a system that captures images of dashboards/screens and extracts key labeled values (e.g., “Label AB,” “Label XY”) using a camera.
Our internal team handles the hardware/camera system and uploads images to the cloud.
We are looking for an experienced AI/ML + Azure cloud developer to build the cloud-side inference pipeline using computer vision + OCR techniques. The entire system must be Dockerized and deployed on Azure.
Scope of Work:
☁️ 1. AI/ML Inference Pipeline on Azure
Receive uploaded screen images from Azure Blob or an API
Preprocess images for:
Blur detection
Glare/reflection handling
Shadow correction
Screen cutoff validation
Use object detection (e.g., YOLO) to detect key screen regions ("Label AB", "Label CD", etc.)
Use OCR to extract numeric or alphanumeric values next to those labels
Match labels → values dynamically (layout agnostic)
Return structured JSON output including:
Detected values
Confidence scores
Any errors or quality issues flagged
? 2. Error & Validation Handling
Flag and tag issues such as:
Image is too blurry
Value not detected
Label missing
Low OCR confidence
Screen not fully visible
Provide this metadata in a clean JSON format
Example:
json
Copy
Edit
{
"label_ab": {
"value": "97",
"confidence": 0.91
},
"label_cd": {
"value": null,
"error": "Label not detected"
},
"image_quality": {
"sharpness_score": 28.5,
"glare_detected": true,
"issues": ["Blurry", "Cutoff at top"]
}
}
? 3. API Development (for mobile app integration)
REST API endpoints to:
Receive an image from the device
Return extracted values + validation flags
(Optional) retrieve previous results by ID or timestamp
API must include image error feedback that our mobile app team can consume and display to users during or after upload.
? 4. Dockerization & Deployment
Package the full pipeline into a Docker container
Deploy to Azure Container Instance or similar
Provide documentation for:
Building and running the container
Updating the model or logic
Environment variables and settings
? 5. Feedback Loop + Retraining Folder Setup
Save flagged/failed cases in a retraining-friendly folder structure
bash
Copy
Edit
/flagged_cases/
/retrain_data/
(Optional) provide a script or structure for retraining the model with new labeled data later
Support manual model upgrading (drop-in new model + rebuild Docker)
?️ Tech Stack Required:
Python (preferred)
OpenCV, PyTorch or TensorFlow
YOLOv5/YOLOv8 or similar detection model
OCR: EasyOCR, Tesseract, or PaddleOCR
Azure Blob Storage, Azure Functions or HTTP Trigger
Azure Container Instance (ACI) or App Service
REST API: FastAPI or Flask
Docker
✅ What We Provide:
Sample labeled screen images (2 dashboard layouts)
Base model checkpoint (if needed)
Azure environment + access
Input/output format specs
Our team handles image capture and uploads
? Deliverables:
Dockerized AI pipeline
Deployed on Azure (ACI preferred)
REST API with:
JSON outputs for detected labels + values
Error tagging + image quality flags
Folder structure + simple script for future retraining
Clear deployment + update documentation
✨ Nice to Have (Bonus):
Experience with model versioning
CI/CD familiarity (Docker push → deploy flow)
Ability to collaborate on feedback-based model improvement
Cloud-Based AI Pipeline (YOLO + OCR) for Extracting Data from Dynamic Screens via Image Input
Description:
We are developing a system that captures images of dashboards/screens and extracts key labeled values (e.g., “Label AB,” “Label XY”) using a camera.
Our internal team handles the hardware/camera system and uploads images to the cloud.
We are looking for an experienced AI/ML + Azure cloud developer to build the cloud-side inference pipeline using computer vision + OCR techniques. The entire system must be Dockerized and deployed on Azure.
Scope of Work:
☁️ 1. AI/ML Inference Pipeline on Azure
Receive uploaded screen images from Azure Blob or an API
Preprocess images for:
Blur detection
Glare/reflection handling
Shadow correction
Screen cutoff validation
Use object detection (e.g., YOLO) to detect key screen regions ("Label AB", "Label CD", etc.)
Use OCR to extract numeric or alphanumeric values next to those labels
Match labels → values dynamically (layout agnostic)
Return structured JSON output including:
Detected values
Confidence scores
Any errors or quality issues flagged
? 2. Error & Validation Handling
Flag and tag issues such as:
Image is too blurry
Value not detected
Label missing
Low OCR confidence
Screen not fully visible
Provide this metadata in a clean JSON format
Example:
json
Copy
Edit
{
"label_ab": {
"value": "97",
"confidence": 0.91
},
"label_cd": {
"value": null,
"error": "Label not detected"
},
"image_quality": {
"sharpness_score": 28.5,
"glare_detected": true,
"issues": ["Blurry", "Cutoff at top"]
}
}
? 3. API Development (for mobile app integration)
REST API endpoints to:
Receive an image from the device
Return extracted values + validation flags
(Optional) retrieve previous results by ID or timestamp
API must include image error feedback that our mobile app team can consume and display to users during or after upload.
? 4. Dockerization & Deployment
Package the full pipeline into a Docker container
Deploy to Azure Container Instance or similar
Provide documentation for:
Building and running the container
Updating the model or logic
Environment variables and settings
? 5. Feedback Loop + Retraining Folder Setup
Save flagged/failed cases in a retraining-friendly folder structure
bash
Copy
Edit
/flagged_cases/
/retrain_data/
(Optional) provide a script or structure for retraining the model with new labeled data later
Support manual model upgrading (drop-in new model + rebuild Docker)
?️ Tech Stack Required:
Python (preferred)
OpenCV, PyTorch or TensorFlow
YOLOv5/YOLOv8 or similar detection model
OCR: EasyOCR, Tesseract, or PaddleOCR
Azure Blob Storage, Azure Functions or HTTP Trigger
Azure Container Instance (ACI) or App Service
REST API: FastAPI or Flask
Docker
✅ What We Provide:
Sample labeled screen images (2 dashboard layouts)
Base model checkpoint (if needed)
Azure environment + access
Input/output format specs
Our team handles image capture and uploads
? Deliverables:
Dockerized AI pipeline
Deployed on Azure (ACI preferred)
REST API with:
JSON outputs for detected labels + values
Error tagging + image quality flags
Folder structure + simple script for future retraining
Clear deployment + update documentation
✨ Nice to Have (Bonus):
Experience with model versioning
CI/CD familiarity (Docker push → deploy flow)
Ability to collaborate on feedback-based model improvement