Sunbird Saral Overview
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
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
Source Code Repository
Saral SDK Source Code Repository
Application Architecture of Saral App
AI/ML model embedded within Android Application for predicting Handwritten digits.
Each layout is configurable in the backend as JSON. Refer
for more details.
Saral SDK and Application Architecture
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
component can be reused for Android and React Native App development with handwritten digits and OMR bubbles prediction capabilities.
Handwritten Digits ML Model
Handwritten Digits Machine Learning model is build using Python,Keras ,Tensor-flow technologies.
This Machine Learning model is built on
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.
is used for capturing ROI’s(Region Of Interest) and processing images before passing them to ML model for prediction.
OMR(Optical Mark Recognition) Detection
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.
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