Cite this DOI

Dr.G.Sambasivarao, D. (2022). Diagonal Feature Extraction Based Handwritten Character System Using Neural Network. *Journal of Science & Technology*, *7*(6), 60–79. https://doi.org/10.46243/jst.2022.v7.i06.pp60-79

@article{drgsambasivarao2022diagonal,
  author    = {Dr.G.Sambasivarao, Dr.G.Sambasivarao},
  title     = {{Diagonal Feature Extraction Based Handwritten Character System Using Neural Network}},
  journal   = {Journal of Science \& Technology},
  year      = {2022},
  month     = {aug},
  volume    = {7},
  number    = {6},
  pages     = {60--79},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2022.v7.i06.pp60-79},
  url       = {https://doi.org/10.46243/jst.2022.v7.i06.pp60-79},
  language  = {en},
  abstract  = {A handwritten character recognition system using multilayer Feed forward neural network is proposed in this paper. The character data set suitable for recognizing postal addresses contains 38 elements which include 26 alphabets, 10 numerals and 2 symbols. Fifteen different handwritten data sets were used for training the neural network for classification and recognition of the characters. Three different orientations, namely, horizontal, vertical and diagonal directions are used for extracting 54 features from each character. The trained neural recognition system is tested for various inputs and found to perform well. The diagonal orientation for feature extraction is identified to be the most suitable method as it yields higher recognition accuracy. The proposed system will aid applications for postal/parcel address recognition and conversion of any hand written document into structural text form}
}

TY  - JOUR
TI  - Diagonal Feature Extraction Based Handwritten Character System Using Neural Network
AU  - Dr.G.Sambasivarao, Dr.G.Sambasivarao
JO  - Journal of Science & Technology
PY  - 2022
DA  - 2022/08/23/
VL  - 7
IS  - 6
SP  - 60
EP  - 79
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - A handwritten character recognition system using multilayer Feed forward neural network is proposed in this paper. The character data set suitable for recognizing postal addresses contains 38 elements which include 26 alphabets, 10 numerals and 2 symbols. Fifteen different handwritten data sets were used for training the neural network for classification and recognition of the characters. Three different orientations, namely, horizontal, vertical and diagonal directions are used for extracting 54 features from each character. The trained neural recognition system is tested for various inputs and found to perform well. The diagonal orientation for feature extraction is identified to be the most suitable method as it yields higher recognition accuracy. The proposed system will aid applications for postal/parcel address recognition and conversion of any hand written document into structural text form
DO  - 10.46243/jst.2022.v7.i06.pp60-79
UR  - https://doi.org/10.46243/jst.2022.v7.i06.pp60-79
ER  -

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    "URL": "https://doi.org/10.46243/jst.2022.v7.i06.pp60-79",
    "title": "Diagonal Feature Extraction Based Handwritten Character System Using Neural Network",
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "A handwritten character recognition system using multilayer Feed forward neural network is proposed in this paper. The character data set suitable for recognizing postal addresses contains 38 elements which include 26 alphabets, 10 numerals and 2 symbols. Fifteen different handwritten data sets were used for training the neural network for classification and recognition of the characters. Three different orientations, namely, horizontal, vertical and diagonal directions are used for extracting 54 features from each character. The trained neural recognition system is tested for various inputs and found to perform well. The diagonal orientation for feature extraction is identified to be the most suitable method as it yields higher recognition accuracy. The proposed system will aid applications for postal/parcel address recognition and conversion of any hand written document into structural text form",
    "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.2022.v7.i06.pp60-79 gives all four in one JSON answer.