Latest Global Blindness and VI prevalence figures published in Lancet Vision Atlas. https://atlas.iapb.org/news/latest-global-blindness-vi-prevalence-figures-published-lancet//. Accessed 8 Feb 2020
Rantala J, Raisamo R, Lylykangas J, Surakka V, Raisamo J, Salminen K, Pakkanen T, Hippula A (2009) Methods for presenting Braille characters on a mobile device with a touch-screen and tactile feedback. IEEE Trans Haptics 2(1):28–39
Article
Google Scholar
Grussenmeyer W, Folmer E (2017) Accessible touchscreen technology for people with visual impairments : a survey. ACM Trans Access Comput 9(2):1–31
Article
Google Scholar
Rodrigues A, Santos A, Montague K, and Guerreiro T (2017) Improving Smartphone Accessibility with Personalizable Static Overlays. In: Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility- ASSETS. Baltimore, MD, USA, ACM, p. 37–41, October 2017.
Jafri R, Khan MM (2018) User-centered design of a depth data based obstacle detection and avoidance system for the visually impaired. Human-centric Comput Inform Sci 8(1):1–14
Article
Google Scholar
Cao D, Chen Z, Gao L (2020) An improved object detection algorithm based on multi-scaled and deformable convolutional neural networks. Human-centric Computing and Information Sciences, Springer Open 10(14):1–22
Google Scholar
Alqarni MA, Chauhdary SH, Malik MN, Ehatisham-ul-Haq M, Azam MA (2020) Identifying smartphone users based on how they interact with their phones. Human-centric Comput Inform Sci 10(1):1–14
Article
Google Scholar
Catania F (2020) Conversational Technology and Natural Language Visualization for Children's Learning. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, USA, ACM, p. 1–7. April 2020
Šepić B, Ghanem A, Vogel S (2015) Braille easy: one-handed Braille keyboard for smartphones. Stud Health Technol Inform 217:1030–1035
Google Scholar
Be My Eyes-See the world together. https://www.bemyeyes.com/. Accessed 20 Feb 2020
BeSpecular . Available at: https://www.bespecular.com/. Accessed 12 Mar 2020
World D, TapTapSee Camera App for Visually Impaired _ Disabled World. https://www.disabled-world.com/assistivedevices/apps/taptapsee.php. Accessed 15 Feb 2020
Google, KNFB Reader App features the best OCR. https://www.knfbreader.com/. Accessed 10 Feb 2020
Kacorri H, Kitani KM, Bigham JP, and Asakawa C (2017) People with Visual Impairment Training Personal Object Recognizers : Feasibility and Challenges. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver Colorado, USA, ACM, p. 5839–5849, May 2017
Panda SP, Nayak NK, Rai CS (2020) A survey on speech synthesis techniques in Indian languages. Multim Syst 26:453–478
Article
Google Scholar
Matoušek J, Krňoul Z, Campr M, Zajíc Z, Hanzlíček Z, Grůber M, Kocurová M (2020) Speech and web-based technology to enhance education for pupils with visual impairment. J Multimodal User Interf 14:219–230
Article
Google Scholar
Verma P, Singh R, Singh AK (2013) A framework to integrate speech based interface for blind web users on the websites of public interest. Human-Centric Comput Inform Sci 3(1):1–21
Article
Google Scholar
Stella J, Valsan KS (2018) Text to Braille conversion: a survey. Int J Manag Appl Sci 4(1):15–18
Google Scholar
Frey B, Southern C, and Romero M (2011) Braille Touch : Mobile Texting for the Visually Impaired. In: International Conference on Universal Access in Human-Computer Interaction, Springer, Berlin, Heidelberg, p. 19–25, July 2011
Mascetti S, Bernareggi C, and Belotti M (2011) TypeInBraille : a Braille-based typing application for touch-screen devices. In: The proceedings of the 13th international ACM SIGACCESS conference on Computers and accessibility, Dundee Scotland, UK, p. 295–296, October 2011
Mattheiss E, Regal G, Schrammel J, Garschall M, Tscheligi M (2015) EdgeBraille: Braille-based text input for touch devices. Journal of Assistive Technologies 9(3):147–158
Article
Google Scholar
Jayant C, Acuario C, Johnson W, Hollier J, and Ladner R (2010) VBraille : Haptic Braille Perception using a Touch-screen and Vibration on Mobile Phones. In: Proceedings of the 12th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS. Orlando, Florida, USA: ACM, p. 295–296, October 2010
Azenkot S (2014) Eyes-Free Input on Mobile Devices. Dissertation, University of Washington
McNaughton J, Crick T, Hatch A (2017) Determining device position through minimal user input. Human-centric Comput Inform Sci 7(1):1–37
Article
Google Scholar
Gidh VY, Latey SM, Roy A, Shah K, Ingle S (2013) Braille Calculator. Int J Eng Comput Sci 2(2):1–3
Google Scholar
Siqueira J, De-Melo-Nunes FAA, Silva CRG, De-Oliveira-Berretta L, Ferreira CBR, Félix IM, and Luna MM (2016) Braille Écran: A Braille Approach to Text Entry on Smartphones. In: IEEE 40th Annual Computer Software and Applications Conference, IEEE, p. 608–609, June 2016.
Alnfiai M, Sampalli S (2017) BrailleEnter: A Touch Screen Braille Text Entry Method for the Blind. Procedia Comput Sci 109:257–264
Article
Google Scholar
Subash NS, Nambiar S, and Kumar V (2012) Braille Key: An alternative Braille text input system: Comparative study of an innovative simplified text input system for the visually impaired. In: 4th International Conference on Intelligent Human Computer Interaction: Advancing Technology for Humanity, (IHCI), p. 4–7, December 2012.
Shabnam M, Govindarajan S (2016) Gesture recognition algorithm: Braille-coded gesture patterns for touch screens: eyedroid. Indian J Sci Technol 9(33):1–9
Article
Google Scholar
Alnfiai M, Sampalli S (2016) SingleTap Braille: developing a text entry method based on Braille patterns using a single tap. Procedia Comput Sci 94:248–255
Article
Google Scholar
Alnfiai M, Sampalli S (2017) Improved SingleTap Braille : Developing a single tap text entry method based on Grade 1 and 2 Braille encoding. J Ubiquit Syst Perv Netw 9(1):23–31
Google Scholar
Alnfiai M, Sampalli S (2019) Braille Tap : Developing a Calculator Based on Braille Using Tap Gestures. Universal Access in Human-Computer Interaction. Springer, Designing Novel Interactions, Vancouver, Canada, pp 213–223
Google Scholar
Leporini B, Buzzi MC, and Buzzi M (2012) Interacting with mobile devices via VoiceOver: usability and accessibility issues. In Proceedings of the 24th Australian Computer-Human Interaction Conference, Melbourne, Australia, ACM, pp. 339–348, 2012
Karmel A, Sharma A, Garg D (2019) IoT based assistive device for deaf, dumb and blind people. Procedia Comput Sci 165:259–269
Article
Google Scholar
Boruah A, Kakoty NM, Ali T (2018) Object recognition based on surface detection-a review. Procedia Comput Sci 133:63–74
Article
Google Scholar
Guerreiro T, Lagoá P, Santana P, Gonçalves D, and Jorge J (2008) NavTap and BrailleTap: Non-Visual Texting Interfaces. In: Rehabilitation Engineering and Assistive Technology Society of North America Conference (Resna), USA, p. 1–10
Bier A, Sroczyński Z (2019) Rule based intelligent system verbalizing mathematical notation. Multimedia Tools and Applications, Springer 78(19):28089–28110
Article
Google Scholar
Nahar L, Jaafar A, Ahamed E, Kaish ABMA (2015) Design of a Braille learning application for visually impaired students in Bangladesh. Assis Technol 27(3):172–182
Article
Google Scholar
Iqbal MZ, Shahid S, and Naseem M (2017) Interactive Urdu Braille Learning System for Parents of Visually Impaired Students. In: Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility, Baltimore: ACM, p. 327–328, October 2017
Parvathi K, Samal BM, and Das JK (2015) Odia Braille : Text Transcription via Image Processing. In: International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), USA: IEEE, p. 138–143, Feb 2015
Al-Shamma SD, and Fathi S (2010) Arabic Braille recognition and transcription into text and voice. In: 5th Cairo International Biomedical Engineering Conference, (CIBEC). Cairo, Egypt: IEEE, p. 227–231, Dec 2010
Devi GG, and Sathyanarayanan G (2018) Braille Document Recognition in Southern Indian Languages–A Review. In 2018 Fourth International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), IEEE, pp. 1–4. Feb 2018
Nasib AU, Kabir H, Ahmed R, and Uddin J (2018) A real time speech to text conversion technique for bengali language. In: International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). Rajshahi, Bangladesh, IEEE, pp. 1–4. Sept 2018
Rasheed I, Gupta V, Banka H, and Kumar C (2018) Urdu Text Classification: A comparative study using machine learning techniques. In Thirteenth International Conference on Digital Information Management (ICDIM). Berlin, Germany, IEEE, pp. 274–278. Sept 2018
Wang X, Zhong J, Cai J, Liu H. and Qian Y (2019) CBConv: Service for Automatic Conversion of Chinese Characters into Braille with High Accuracy. In: The 21st International ACM SIGACCESS Conference on Computers and Accessibility. Pittsburgh, USA, ACM, pp. 566–568, Oct 2019
Bengio Y, Lamblin P, Popovici D, and Larochelle H (2006) Greedy Layer-Wise Training of Deep Networks. In: NIPS'06: In: Proceedings of the 19th International Conference on Neural Information Processing Systems, British Columbia, Canada, ACM, p. 153–160, 2006
Jaswal D, Sowmya V, Soman KP (2014) Image Classification Using Convolutional Neural Networks. International Journal of Scientific and Engineering Research 5(6):1661–1668
Article
Google Scholar
Gao X, Zhang J, and Wei Z (2018) Deep Learning for Sequence Pattern Recognition. In: 15th IEEE International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China: IEEE, p. 1–16, Mar 2018
Li T, Zeng X, and Xu S (2014) A deep learning method for Braille recognition. 6th International Conference on Computational Intelligence and Communication Networks, (CICN) 2014, p. 1092–1095, Nov 2014
Murthy VV, and Hanumanthappa M (2018) Improving Optical Braille Recognition in Pre-processing Stage. In: International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India, IEEE, pp. 1–3, Feb 2018
Jha V, Parvathi K (2019) Braille Transliteration of hindi handwritten texts using machine learning for character recognition. Int J Sci Technol Res 8(10):1188–1193
Google Scholar
Jha V, Parvathi K (2020) Machine learning based Braille transliteration of odia language. Int J Innov Technol Explor Eng 5:1866–1871
Google Scholar
Perera TDSH, Wanniarachchi WKILI (2018) Optical Braille recognition based on histogram of oriented gradient features and support-vector machine. Int J Eng Sci 8(10):19192–19195
Google Scholar
Li J, Yan X, and Zhang D (2010) Optical Braille Recognition with Haar Wavelet Features and Support-Vector Machine. In: International Conference on Computer, Mechatronics, Control and Electronic Engineering. Changchun, China: IEEE, p. 64–67, Aug 2010
Udapola UBHS, and Liyanage SR (2017) Braille Messenger : Adaptive Learning Based Non- Visual Touch Screen Input for the Blind Community Using Braille. In: International Conference on Innovations in Info-business and Technology, Ozo, Colombo, Sri Lanka, p. 1–11, Nov 2017
Choudhury AA, Saha R, Shoumo SZH, Tulon SR, Uddin J, and Rahman MK (2018) An Efficient Way to Represent Braille using YOLO Algorithm. In: Joint 7th International Conference on Informatics, Electronics and Vision (ICIEV) and 2nd International Conference on Imaging, Vision and Pattern Recognition (icIVPR), IEEE, pp. 379–383, 2018
Balasuriya BK, Lokuhettiarachchi NP, Ranasinghe ARMDN, Shiwantha KDC, and Jayawardena C (2017) Learning Platform for Visually Impaired Children through Artificial Intelligence and Computer Vision. In: 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) Learning, Colombo, Sri Lanka: IEEE, p. 1–7, Dec 2017
Zhang J, Wei Z, Chen J (2018) Subject Section A distance-based approach for testing the mediation effect of the human microbiome. Bioinformatics 34(11):1875–1883
Article
Google Scholar
Pan SJ (2010) Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Article
Google Scholar
Simonyan K, and Zisserman A (2015) Very Deep Convolutional Networks For Lrage-Scale Image Recognition. In: Conf. on Learning Representations (ICLR), San Diego, CA, USA, p. 1–14, 2015
Torrey L, Shavlik J (2010) Transfer Learning. Handbook of research on machine learning applications and trends: algorithms, methods, and techniques, vol 2. IGI Global, Hershey, USA, pp 242–264
Chapter
Google Scholar
Bengio Y, and Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, New York City, USA, p. 2278–2324, June 1998
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, and Rabinovich A (2015) Going Deeper with Convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, Massachusetts, USA, IEEE, p. 1–9, June 2015.
Zeiler MD, and Fergus R (2014) Visualizing and Understanding Convolutional Networks. In: 3th European conference on computer vision, Zurich, Switzerland, Springer, p. 818–833, September 2014.
Network of Networks - Encyclopedia. https://www.encyclopedia.com/computing/news-wires-white-papers-and-books/network-networks/. Accessed 5 Feb 2020.
Song W, Zhang L, Tian Y, Fong S, Liu J, Gozho A (2020) CNN-based 3D object classification using Hough space of LiDAR point clouds. Human-centric Comput Inform Sci 10(1):1–14
Article
Google Scholar
Kingma DP, and Ba J (2015) ADAM: A Method For Stochastic Optimization.In: Conf. on Learning Representations (ICLR). San Diego, CA, USA, p. 1–15, May 2015
Pranckevičius T, Marcinkevičius V (2017) Comparison of Naïve Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification. Baltic J Modern Computing 5(2):221–232
Article
Google Scholar
Tharwat A (2019) Parameter investigation of support vector machine classifier with kernel functions. Knowl Inform Syst 61(3):1269–1302
Article
MathSciNet
Google Scholar
Song W, Zhang L, Tian Y, Fong S, Liu J, and Gozho A (2014) KNN Algorithm with Data-Driven k Value KNN Algorithm with Data-Driven k Value. In: International Conference on Advanced Data Mining and Applications, Guilin, China, Springer, p. 499–512, December 2014
Choi JY, Yoo TK, Seo JG, Kwak J, Um TT, Rim TH (2017) Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PLoS ONE 12(11):1–16
Google Scholar
Salim M (2018) Deep Neural Network Models for Image Classification and Regression. Dissertation, University of Trento
Zhang S, Bao Y, Zhou P, Jiang H, and Dai L (2014) Improving Deep Neural Networks For LVCSR Using Dropout And Shrinking Structure. In: IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), IEEE, p. 6849–6853, May 2014
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
MathSciNet
MATH
Google Scholar