Cite this DOI

Thorat, D. S., Tamsekar, D. P., & Patil, D. P. (2018). Recognition of Handwriting Characters Using an Artificial Neural Network. *Journal of Science & Technology*, *03*(05), 81–90. https://doi.org/10.46243/jst.2018.v3.i05.pp81-90

@article{thorat2018recognition,
  author    = {Thorat, Dr S.B. and Tamsekar, Dr. P.B. and Patil, Dr. P.R.},
  title     = {{Recognition of Handwriting Characters Using an Artificial Neural Network}},
  journal   = {Journal of Science \& Technology},
  year      = {2018},
  month     = {sep},
  volume    = {03},
  number    = {05},
  pages     = {81--90},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2018.v3.i05.pp81-90},
  url       = {https://doi.org/10.46243/jst.2018.v3.i05.pp81-90},
  language  = {en},
  abstract  = {Research in the area of handwriting recognition has several applications, including the digitization of handwritten documents and the ability to use handwriting as an input method for gadgets. In this project, an Artificial Neural Network (ANN) was used to produce a handwriting recognition system, and the Streamlit library was used to develop a graphical user interface (GUI). We preprocessed the photos by scaling, turning to grayscale, and normalising pixel values using a dataset of handwritten numbers from Kaggle. Two hidden layers with ReLU activation and Softmax activation for the output layer made up the ANN model's design. After adding the pytesseract library, the model had a 90\% accuracy rate on the test dataset. Users could draw a digit and get results for digit recognition using the GUI interface. Future studies could concentrate on increasing precision and broadening the system's ability to recognise handwritten text. The overall potential of ANN and GUI technologies for handwriting recognition applications is demonstrated by this project}
}

TY  - JOUR
TI  - Recognition of Handwriting Characters Using an Artificial Neural Network
AU  - Thorat, Dr S.B.
AU  - Tamsekar, Dr. P.B.
AU  - Patil, Dr. P.R.
JO  - Journal of Science & Technology
PY  - 2018
DA  - 2018/09/18/
VL  - 03
IS  - 05
SP  - 81
EP  - 90
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Research in the area of handwriting recognition has several applications, including the digitization of handwritten documents and the ability to use handwriting as an input method for gadgets. In this project, an Artificial Neural Network (ANN) was used to produce a handwriting recognition system, and the Streamlit library was used to develop a graphical user interface (GUI). We preprocessed the photos by scaling, turning to grayscale, and normalising pixel values using a dataset of handwritten numbers from Kaggle. Two hidden layers with ReLU activation and Softmax activation for the output layer made up the ANN model's design. After adding the pytesseract library, the model had a 90% accuracy rate on the test dataset. Users could draw a digit and get results for digit recognition using the GUI interface. Future studies could concentrate on increasing precision and broadening the system's ability to recognise handwritten text. The overall potential of ANN and GUI technologies for handwriting recognition applications is demonstrated by this project
DO  - 10.46243/jst.2018.v3.i05.pp81-90
UR  - https://doi.org/10.46243/jst.2018.v3.i05.pp81-90
ER  -

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    "URL": "https://doi.org/10.46243/jst.2018.v3.i05.pp81-90",
    "title": "Recognition of Handwriting Characters Using an Artificial Neural Network",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Research in the area of handwriting recognition has several applications, including the digitization of handwritten documents and the ability to use handwriting as an input method for gadgets. In this project, an Artificial Neural Network (ANN) was used to produce a handwriting recognition system, and the Streamlit library was used to develop a graphical user interface (GUI). We preprocessed the photos by scaling, turning to grayscale, and normalising pixel values using a dataset of handwritten numbers from Kaggle. Two hidden layers with ReLU activation and Softmax activation for the output layer made up the ANN model's design. After adding the pytesseract library, the model had a 90% accuracy rate on the test dataset. Users could draw a digit and get results for digit recognition using the GUI interface. Future studies could concentrate on increasing precision and broadening the system's ability to recognise handwritten text. The overall potential of ANN and GUI technologies for handwriting recognition applications is demonstrated by this project",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2018.v3.i05.pp81-90 gives all four in one JSON answer.