DEVANAGARI CHARACTER RECOGNITION USING FEATURE SET SELECTION ALGORITHM
DOI:
https://doi.org/10.61841/vcrt8725Keywords:
artificial neural network, a pixel intensity, , improved rule based feature set selection algorithm, devanagari hand written characterAbstract
Presently these days recognizing the handwritten character recognition is getting high essentialness in light of various applications like educational field, digitized signature verification, bank processing, postal code acknowledgment, electronic library and so on. exceptionally less work is ac-counted in the research of Devanagari hand written character acknowledgment, so that there is an enormous extent of research right now. Some potential challenges adding to the unlawful execution of different frameworks for seeing deciphered characters are: various shapes, broken characters, various tendencies and measures, and so on. To overcome these types of issues, initially, we introduce a pixel intensity histogram based feature for the special character recognition, it identifies the special symbols and characters and different types of characters. Further, we used selection process done by improved rule based feature set selection algorithm. Dataset is collected and with help of this proposed improved rule based feature set selection algorithm, the accuracy of character identification is improved. Further, we use recurrent-artificial neural network classifier for classification and recognition process to classify different types of handwritten characters. The performance of proposed model is compared with the existing designs in terms of higher accuracy and speed in classification and recognition.
Downloads
References
[1] Jung, E., Chikontwe, P., Zong, X., Lin, W., Shen, D. and Park, S.H., 2019. Enhancement of perivascular spaces using densely connected deep convolutional neural network. IEEE Access, 7, pp.18382-18391.
[2] Chen, J., Yang, R., Zhang, C., Zhang, L. and Zhang, Q., 2019. DeepGly: A Deep Learning Framework With Recurrent and Convolutional Neural Networks to Identify Protein Glycation Sites From Imbalanced Data. IEEE Access, 7, pp.142368-142378.
[3] Isogawa, K., Ida, T., Shiodera, T. and Takeguchi, T., 2017. Deep shrinkage convolu-tional neural network for adaptive noise reduction. IEEE Signal Processing Letters, 25(2), pp.224-228
[4] Alali, M., Sharef, N.M., Murad, M.A.A., Hamdan, H. and Husin, N.A., 2019. Narrow Convolutional Neural Network for Arabic Dialects Polarity Classification. IEEE Access, 7, pp.96272-96283
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
