EasyOCR Detection System for Raspberry Pi/Embedded -- 2
Budget: ₹12,500 – ₹37,500 INR
Objective:
Develop an OCR-based system to extract numerical/text data from various digital screens (e.g., industrial displays, dashboards) every 30 seconds, ensuring adaptability to different layouts, fonts, and formats.
Ideally we will want to run it on a RPI 2W to a RPI 4 1GB variant.
Key Requirements:
Screen Layout Adaptability
Scrape & analyze various screen layouts from different brands to improve system flexibility.
Implement AI-based text detection (YOLO/EAST) to handle layout variations.
OCR Processing Pipeline
Frame capture every 30 seconds, ensuring clear, stable images.
Use EasyOCR/Tesseract with pre-processing (contrast enhancement, thresholding, de-noising).
Apply post-processing corrections (regex filtering, validation rules) for improved accuracy.
Optimization for Edge Devices
Ensure efficient processing on Raspberry Pi, Jetson Nano, or low-power devices.
Convert models to TensorFlow Lite/ONNX for lightweight execution.
Data Output & Integration
Structure extracted data in JSON format.
Support local storage & cloud sync options.
Challenges & Mitigations:
Challenge Solution
Varied Layouts YOU HAVE TO Scrape data FROM INTERNET on screen types + use AI-based text detection
Glare & Noise Apply pre-processing (adaptive thresholding, contrast adjustment)
OCR Errors Use post-processing corrections (regex, validation rules)
Hardware Constraints Optimize models (TensorFlow Lite/ONNX) for edge devices
Tech Stack:
Languages: Python (OpenCV, TensorFlow, PyTorch), C++ (optional)
Libraries: OpenCV, EasyOCR/Tesseract, YOLO/EAST for text detection
Deployment: Raspberry Pi 4/2W
Deliverables:
✅ Adaptive OCR Pipeline (Capture → Pre-process → Extract → Validate)
✅ AI Model for Dynamic Text Detection (if required)
✅ Optimized Deployment Package for Edge Devices
✅ Documentation & Installation Guide
Example Use Cases (We will share exact use case after NDA) :
? Industrial Equipment Screens: Read meters & control panels.
? Stock Market Displays: Extract live ticker data.
? IoT Display Panels: Capture sensor values for automation.
Develop an OCR-based system to extract numerical/text data from various digital screens (e.g., industrial displays, dashboards) every 30 seconds, ensuring adaptability to different layouts, fonts, and formats.
Ideally we will want to run it on a RPI 2W to a RPI 4 1GB variant.
Key Requirements:
Screen Layout Adaptability
Scrape & analyze various screen layouts from different brands to improve system flexibility.
Implement AI-based text detection (YOLO/EAST) to handle layout variations.
OCR Processing Pipeline
Frame capture every 30 seconds, ensuring clear, stable images.
Use EasyOCR/Tesseract with pre-processing (contrast enhancement, thresholding, de-noising).
Apply post-processing corrections (regex filtering, validation rules) for improved accuracy.
Optimization for Edge Devices
Ensure efficient processing on Raspberry Pi, Jetson Nano, or low-power devices.
Convert models to TensorFlow Lite/ONNX for lightweight execution.
Data Output & Integration
Structure extracted data in JSON format.
Support local storage & cloud sync options.
Challenges & Mitigations:
Challenge Solution
Varied Layouts YOU HAVE TO Scrape data FROM INTERNET on screen types + use AI-based text detection
Glare & Noise Apply pre-processing (adaptive thresholding, contrast adjustment)
OCR Errors Use post-processing corrections (regex, validation rules)
Hardware Constraints Optimize models (TensorFlow Lite/ONNX) for edge devices
Tech Stack:
Languages: Python (OpenCV, TensorFlow, PyTorch), C++ (optional)
Libraries: OpenCV, EasyOCR/Tesseract, YOLO/EAST for text detection
Deployment: Raspberry Pi 4/2W
Deliverables:
✅ Adaptive OCR Pipeline (Capture → Pre-process → Extract → Validate)
✅ AI Model for Dynamic Text Detection (if required)
✅ Optimized Deployment Package for Edge Devices
✅ Documentation & Installation Guide
Example Use Cases (We will share exact use case after NDA) :
? Industrial Equipment Screens: Read meters & control panels.
? Stock Market Displays: Extract live ticker data.
? IoT Display Panels: Capture sensor values for automation.