Open Access
Issue
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
Article Number 01077
Number of page(s) 9
DOI https://doi.org/10.1051/e3sconf/202343001077
Published online 06 October 2023
  1. Chanhum Park, Jiho Choi, and Kang Ryoung Park, “Deep Feature- Based Three-Stage Detection of Banknotes and Coins for Assisting Visually Impaired People,” October 21,2020.Digital Object Identifier 10.1109/ACCESSS.2020.3029 [Google Scholar]
  2. S. Mittal and S. Mittal, “Indian Banknote Recognition using Convolu- tional Neural Network,” 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), Bhimtal, India, 2018, pp. 1-6, doi: 10.1109/IoT-SIU.2018.8519888. [Google Scholar]
  3. S.K. Katiyar and P.V. Arun “Comparative analysis of common edge detection techniques in the context of object extraction.” India IEEE TGRS, 2017. [Google Scholar]
  4. N.A. J. Sufri, N. A. Rahmad, N. F. Ghazali, N. Shahar “Vision Based System for Banknote Recognition Using Different Machine Learning and Deep Learning Approach” 2019 IEEE 2019. [Google Scholar]
  5. F. M. Hasanuzzaman, X. Yang and Y. Tian, “Robust and effective component-based banknote recognition for the blind“, IEEE Transac- tions, 2018. [Google Scholar]
  6. V. Abburu, S. Gupta, S. Rimitha, M. Mulimani, and S. Koolagudi. “Currency recognition system using image processing“ IEEE Computer Society, (2017). [Google Scholar]
  7. Qian Zhang, Wei Qi Yan, Mohan Kankanhalli, “Overview of currency recognition using deep learning”. Journal of Banking and Financial Technology volume 3,(2019). [Google Scholar]
  8. Iyad Abu and Doush Sahar.”Currency recognition using a smartphone: Comparison between color SIFT and grayscale SIFT algorithms”. Jour- nal of King Saud, 2017 [Google Scholar]
  9. Dittimi, Tamarafinide V., Ali K. Hmood, and Ching Y. Suen. “Multiclass SVM based gradient feature for banknote recognition.” 2017 Interna- tional Conference on Industrial Technology (ICIT), IEEE 2017. [Google Scholar]
  10. Kitagawa, Ryutaro, Edgar SimoSerra, Hiroshi Matsuki, Naotake Natori, and Hiroshi Ishikawa. “Banknote portrait detection using convolutional neural network.” IEEE 2017. [Google Scholar]
  11. Fumiaki Takeda, Lalita Sakoobunthu and Hironobu Satou, “Thai ban- knote recognition using neural network and continues learning by DSP unit” in Information and Engineering System.Springer 2013 [Google Scholar]
  12. Kamal, Snigdha, Simarpreet Singh Chawla, Nidhi Goel, and Balasubra-manian Raman. “Feature extraction and identification of Indian currency notes.” IEEE 2015. [Google Scholar]
  13. Jin, O., et al. Recognition of New and Old Banknotes Based on SMOTE and SVM. IEEE Int Conf, 2017. [Google Scholar]
  14. Gundavarapu, M.R., Ineni, S.K., Sathvika, K., Keshava, G.S., Charan, U.R.,Journal of Physics: Conference Series, 2325 (2022). [Google Scholar]
  15. G. M. Rao, C. Sowmya, D. Mamatha, P. A. Sujasri, S. Anitha and R. Alivela, Sign Language Recognition using LSTM and Media Pipe, 7th International Conference on Intelligent Computing and Control Systems (ICICCS),1086-1091,Madurai, India, (2023). [Google Scholar]
  16. Chandra Sekhar Reddy P, Sakthidharan G, Kanimozhi Suguna S, Mannar Mannan J, Varaprasada Rao P, International Journal of Engineering and Advanced Technology. 8, (2019). [Google Scholar]
  17. P. Chandra Sekhar Reddy, B. Eswara Reddy and V. Vijaya Kumar, International Journal of Image, Graphics and Signal Processing. 4, (2012). [Google Scholar]
  18. Chandrika Lingala, and Karanam Madhavi et.al, “A Survey on Cardivascular Prediction using Variant Machine learning Solutions. E3S Web of Conferences 309, 01042, ICMED 2021, (2021). [CrossRef] [EDP Sciences] [Google Scholar]
  19. Kumar, S.K., Reddy, P.D.K., Ramesh, G., Maddumala, V.R. Traitement du Signal, 36 (3) 233-237, (2019). https://doi.org/10.18280/ts.360305. [CrossRef] [Google Scholar]
  20. Somasekar, J Ramesh, G, IJEMS, 29(6) [December 2022], NIScPR-CSIR, India, (2022). [Google Scholar]
  21. Gajula Ramesh, Anusha Anugu, Karanam Madhavi, P. Surekha, Automated Identification and Classification of Blur Images, Duplicate Images Using Open CV. In: Luhach A.K., Jat D.S., Bin Ghazali K.H., Gao XZ., Lingras P. (eds) Advanced Informatics for Computing Research. ICAICR 2020. Communications in Computer and Information Science, vol 1393. Springer, Singapore, (2020). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.