Giter Site home page Giter Site logo

nikolastanojkovski / assistive_bus_helper Goto Github PK

View Code? Open in Web Editor NEW
2.0 1.0 1.0 176.59 MB

An Android application that allows visually impaired people to hear which bus lines are passing next to them.

License: MIT License

Python 72.72% HTML 12.39% Kotlin 14.89%
assistive-technology machine-learning text-to-speech artificial-intelligence easyocr fastspeech2 ocr ocr-recognition visually-impaired-people yolox

assistive_bus_helper's Introduction

Assistive Bus Helper


A Solution for In-house Bus Line Recognition


Contributors: Monika Simjanoska, Kostadin Mishev, Tashko Pavlov, Mario Stojchevski & Nikola Stanojkovski


Daily activities still represent real challenges for visually impaired individuals due to the lack of affordable, appropriate assistive devices. The absence of assistive tools triggers an infinite loop of inappropriate education at its basics, followed by limited lifestyle development that leads to frustration, low confidence, reduced autonomy, and often physical safety risks. There are several methods and devices that are used to guide visually impaired people, and all of them have their advantages and disadvantages.

Assistive Bus Helper is an Android application that provides an interactive manner for the visually impaired individuals to hear which bus line numbers are passing next to them, by just opening up the application and clicking on one button.

The applcation uses integrated python scripts as tools for loading machine learning models and creating predictions and inferences with them. Chaquopy was the Python SDK for Android which enabled this.

The application uses a pipeline of machine learning models which do all the complex processing: FastSpeech 2 as a text-to-speech model of the bus line number, YOLOX and EasyOCR for the OCR recognition and prediction from the automatically taken image.


FastSpeech 2 is the PyTorch text-to-speech machine learning model which generated audio output for the predicted bus line number provided by the other two OCR models.

YOLOX is the machine learning model which was used for predicting which part of the taken image consisted of the bus line number and cropping it.

EasyOCR is the machine learning model which was used for the prediction of the bus number from the cropped image that was provided by YOLOX.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.