📄
Sunbird Saral
  • Sunbird Saral Overview
  • Saral Quick Guide
  • Saral Implementation Manual
    • OMR led scanning - Assessments
    • OCR led scanning - Admissions
  • Saral Transformation Story
  • LEARN
    • Software Requirement
    • Application Architecture
    • Features
      • Configurable Branding
      • Capture AI/ML Training Data
      • Support
      • Share App data
      • Auto Sync
      • Multi-Page support
      • Profile Menu
      • Dynamic Validations
      • Dynamic Tagging
      • Minimal Mode
      • Offline mode
      • App Force Update
      • Review results/marks
      • Firebase Analytics and Crashlytics
      • ML model deployment using Firebase
      • Improved Low light Performance - Manual Edit
      • Vertical Forms Scanning Support
      • Improve Processing Speed for big layouts
      • Admissions Data Capture
      • Securing PII Data Capture - Admissions
    • Specifications
      • Layout specification
      • Backend API Swagger Doc
    • Videos
      • Feature Explanation
        • OMR Layout scanning
        • Auto-Sync
        • Share scan app data
        • Skip feature
        • Support feature
        • Validation feature
        • Incorrect scanning
        • Multi-page feature
        • Branding feature
        • Offline mode
        • Review results/marks
      • Usage by States
        • Gujarat Implementation - Between 39:00 - 40:00 mins
        • Uttar Pradesh(U.P) , Gorakhpur Implementation
    • ML Model Accuracy/Results
  • USE
    • Roadmap
    • Workspace Setup - Playbook
    • Saral App Reference Backend
    • Generating APK from source code
    • Generate AAB(App bundle) from source code
    • Sign already generated APK file with private Key
    • Layout configuration
    • Debug/Run Saral App from Android Studio
    • Saral App Debug Tips
    • Saral App Usage Guidelines
    • Update BASE_URL,apkURL in APK
    • Update BASE_URL,apkURL in AAB
    • Sign already generated AAB(Android App Bundle) file with private key
    • Google Play Store App Publish Considerations
    • Layout Design Guidelines
    • Saral OCR Assets
    • Firebase setup for Saral App Telemetry
    • Firebase setup for TFLite model deployment
    • Alternatives for Saral components
  • ENGAGE
    • Source Code Repository
    • Saral SDK Source Code Repository
    • Tracker
    • Releases
      • v1.0.0-beta.1
      • v1.0.0-beta.2
      • v1.0.0-beta.3
      • v1.0.0-beta.4
      • v1.0.0-beta5
      • v1.5.0
      • v1.5.1
      • v1.5.2
      • v1.5.3
      • v1.5.4
      • v1.5.5
      • v1.5.6
      • v1.5.7
      • v1.5.9
      • v1.6.0
      • v1.6.1
      • v1.6.2
      • v1.7.0
    • Saral - Solution Providers
    • Discuss
  • Experience Saral
  • Dev Environment - Installation & Maintenance
    • Saral Installation Guide (Non-Prod)
    • Saral - Sandbox Maintenance Guide (Non-Prod)
  • Saral Easy Installer
    • Saral Production-Environment User Installation Guide
      • Manual Installation for Prod
      • Automating the Infra provisioning and install of the Saral application
        • Prerequisites
        • What automation does
        • Run installer
        • Post install steps
        • Monitoring-Stack
    • Reference Documents
      • SARAL Infra Requirements & Associated Cost
      • Saral Infra Cost Benefit Analysis
  • Tool for Saral Easy Layout generation and Auto generate ROI json
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  1. LEARN

Application Architecture

Application Architecture of Saral App

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Last updated 2 years ago

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Overall Architecture

Saral SDK and Application Architecture

Handwritten Digits ML Model

Handwritten Digits Machine Learning model is build using Python,Keras ,Tensor-flow technologies.

To embed this model in Android SDK/Application , its converted from HDF5 to TFLite format.

Model is trained on MNIST Handwritten digits open data-set along with handwritten digits from the field.

OMR(Optical Mark Recognition) Detection

is an Android and React Native Software Development kit with core logic to predict handwritten digits and OMR bubbles.

accepts layout JSON as input and enriches the JSON with predictions and sends back the response. Refer Layout specification

component can be reused for Android and React Native App development with handwritten digits and OMR bubbles prediction capabilities.

This Machine Learning model is built on Architecture.

is used for capturing ROI’s(Region Of Interest) and processing images before passing them to ML model for prediction.

For OMR detection uses OpenCV Computer Vision Technology to capture answer sheet images , sub-divide them into individual ROI(Region of Interest Images).

Individual ROI(Region Of Interest) images of answer sheet are processed using in OpenCV and predict if OMR bubble is filled or unfilled using pixel count.

Saral SDK
Saral SDK
Saral SDK
Resnet164
OpenCV
Saral SDK
OTSU thresholding
CQube
Tensorflow Lite