{"references": ["Chaccour, K., & Badr, G. (2016, September). Computer vision guidance system for indoor navigation of visually impaired people. In 2016 IEEE 8th International Conference on Intelligent Systems (IS) (pp. 449-454). IEEE.", "Ali Hassan, E., & Tang, T. B. (2016, July). Smart glasses for the visually impaired people. In International Conference on Computers Helping People with Special Needs (pp. 579- 582). Springer, Cham.", "Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263-7271).", "Walam, S. S., Teli, S. P., Thakur, B. S., Nevarekar, R. R., & Patil, S. M. (2018, April). Object detection and seperation using raspberry pi. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 214-217). IEEE..", "de Oliveira, B. A. G., Ferreira, F. M. F., & da Silva Martins, C. A. P. (2018). Fast and lightweight object detection network: Detection and recognition on resource constrained devices. IEEE Access, 6, 8714-8724..", "Hayat, S., Kun, S., Tengtao, Z., Yu, Y., Tu, T., & Du, Y. (2018, June). A deep learning framework using convolutional neural network for multi-class object recognition. In 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) (pp. 194-198). IEEE..", "Bastomi, R., Ariatama, F. P., Putri, L. Y. A. T., Saputra, S. W., Maulana, M. R., Syai'in, M., ... & Zuliari, E. A. (2019, October). Object Detection and Distance Estimation Tool for Blind People Using Convolutional Methods with Stereovision. In 2019 International Symposium on Electronics and Smart Devices (ISESD) (pp. 1-5). IEEE.", "Jain, N., Yerragolla, S., & Guha, T. (2019, December). Performance Analysis of Object Detection and Tracking Algorithms for Traffic Surveillance Applications using Neural Networks. In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 690-696). IEEE.", "Kumar, M., Harshith, D., Teja, V.(2020). Facial Recognition Smart Glasses for Visually Impaired Persons. IJESC Access. 10 (9).", "Khan, S., Javed, M. H., Ahmed, E., Shah, S. A., & Ali, S. U. (2019, March). Facial recognition using convolutional neural networks and implementation on smart glasses. In 2019 International Conference on Information Science and Communication Technology (ICISCT) (pp. 1-6). IEEE."]}

Someone with the complete loss of vision, for him or her it is very difficult to navigate around places. Roaming around the home is easy as they spend maximum time over there but it becomes difficult when they go outside. In this paper, object identification glasses for blind persons are developed. This is achieved with the help of machine learning techniques. In python, there are libraries like TensorFlow and OpenCV. Using these libraries, it is possible to develop the object prediction model. These scripts can be executed on any computer. Keeping this user-friendliness as well as compactness in mind Raspberry pie 4 is selected for these smart glasses. The camera module on the glass sends the data to the processor. Then it is compared with the objects in the frame with a predefined dataset and it predicts the object. Later it converts text output into an audio signal. The prototype for this is developed and tested.