Oculus: A Smart Wearable for the Visually Impaired

Akash James, Ashish Raman Nayak, Sai Somanath Komanduri, Ujwal P., Prof. Ashwin Kumar U.M.

Abstract


Oculus is a smart wearable that focuses on assisting the visually impaired. It provides features such as Object detection, face classification, classification of Indian currency, environment summarization, and Optical character recognition. All these features are made available to the user through the use of a myriad of technologies such as TensorFlow, MQTT, Tesseract, and OpenCV. The wearable device utilizes a Raspberry Pi interfaced with a Pi camera as the computational unit, which is used to record the surroundings and this video is streamed to server. The results are relayed back to the user through voice using Google Speech Engine. Due to this amalgamation, Oculus proves to be a reliable augmentation to the user.


Keywords


Deep Learning; FaceNet; Google Speech Engine; Image Classification; Inception; Natural Language Processing; Object Detection;Optical Character Recognition; Raspberry Pi 3; Smart Wearable; TensorFlow; Visually Impaired;

Full Text:

PDF

References


https://en.wikipedia.org/wiki/Facial_recognition_system [2] “TensorFlow by Google” https://www.tensorflow.org/ [3] “Towards Data Science” https://towardsdatascience.com/abeginner-introduction-to-tensorflow-part-1-6d139e038278 [4] https://en.wikipedia.org/wiki/Convolutional_neural_netw ork [5] “BUILDING A FACIAL RECOGNITION PIPELINE WITH DEEP LEARNING IN TENSORFLOW”, COLE MURRAY HTTPS://HACKERNOON.COM/BUILDING-A-FACIALRECOGNITION-PIPELINE-WITH-DEEP-LEARNING-INTENSORFLOW-66E7645015B8 [6 ]J. SCHMIDHUBER, "DEEP LEARNING IN NEURAL NETWORKS: AN OVERVIEW", NEURAL NETWORKS, VOL. 61, PP. 85-117, JAN. 2015. [7] KRIZHEVSKY, I. SUTSKEVER, G. E. HINTON, "IMAGENET CLASSIFICATION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS", ADV. NEURAL INF. PROCESS. SYST., PP. 1-9, 2012. [8] M. Abadi et al., "TensorFlow: A System for Large-Scale Machine Learning TensorFlow: A system for large-scale machine learning", 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI'16), pp. 265-284, 2016. [9] Y. Lecun, C. Cortes, C. J. C. Burges, The MNIST Database” Courant Institute NYU, 2014, [online] Available: http://yan.lecun.com/exdb/mnist.




DOI: https://doi.org/10.26483/ijarcs.v9i0.6212

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2018 International Journal of Advanced Research in Computer Science