Handwritten Text Recognition Based On CNN Classification

  • Kasturi Madineni, Prasanna Vasudevan


Handwritten images for characters, digits and special symbols has acknowledged more consideration in research community of pattern recognition due to vast tenders and uncertainty in learning new methods. Primarily, it includes two steps like character recognition and feature extraction, it mainly based on the classification of algorithm. The growth of handwritten charactersareapplicable mainly in cheques, medical prescriptions and tax returns etc., but identifying the handwritten characters are much difficult than the printed characters because each person have different handwriting style. The handwritten recognition mainly based on the convolutional neural network because it provides high accuracy and faster data processing. When compare to capsule neural network and Convolutional neural network the capsule neural network will work faster. The present research working on capsule neural network as classifier, MNIST as dataset with suitable parameters for testing and training for handwritten text recognition.

How to Cite
Kasturi Madineni, Prasanna Vasudevan. (2020). Handwritten Text Recognition Based On CNN Classification. International Journal of Advanced Science and Technology, 29(9s), 6870 - 6880. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/20637