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a |
| 003 - CONTROL NUMBER IDENTIFIER |
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OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20240203122441.0 |
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240203b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
22865 |
| Author |
Zanwar, Shrinivas R. |
| 245 ## - TITLE STATEMENT |
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| Title |
English handwritten character recognition based on ensembled machine learning |
| 250 ## - EDITION STATEMENT |
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| Volume, Issue number |
Vol.104(5), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
USA |
| Name of publisher, distributor, etc. |
Springer |
| Year |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Pagination |
1053-1067p. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
In recent days there are many advancements in optical character recognition (OCR), still, handwritten character recognition remains a challenge due to practices of realizing characters in many ambiguous forms. Currently, multiple algorithms based on deep learning can recognize a character in different languages like English, Devanagari, Chinese, etc. Existing methods have claimed to have an accuracy rate of up to . However, this accuracy is justified only for documents that are printed with fine text, but for degraded image data, these algorithms could not translate handwritten text into a recognized text with satisfactory performance. This work presents a state-of-the-art Novel Naive Propagation (NNP) Classification algorithm along with Harmonized Independent Component Analysis (HICA) and Hybrid Fireflies-Particle Swarm Optimization(HFPSO), which are used to extracting and selecting features from the image data, respectively. Due to the complexity of handwritten characters, the process of character recognition remains challenging. So, we have experimented with an ensembled classifier that combines the various components of the Naive Bayes Propagation Classification algorithm along with the Feed-forward and Backpropagation Neural Network. The experimental results and its analysis with various strategies show the better performance of the proposed system as compared to other techniques. Based on our experimentation we have identified that compared to other character recognition approaches, the Novel Naive Propagation Classifier is more advantageous for creating an automatic HCR system. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 9 (RLIN) |
4642 |
| Topical term or geographic name entry element |
Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
22866 |
| Co-Author |
Bhosale, Yogesh H. |
| 773 0# - HOST ITEM ENTRY |
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| International Standard Serial Number |
2250-2106 |
| Title |
Journal of the institution of engineers (India): Series B |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| URL |
https://link.springer.com/article/10.1007/s40031-023-00917-9 |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Koha item type |
Articles Abstract Database |
