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KNN and PCA Approach for Gujarati Script
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Researchers are very interested in the essential pattern recognition used for offline handwriting recognition. Any handwritten material may be converted into system-editable textual data by identifying concealed forms and understanding the typescripts included within the papers. Gujarati is one of India's 22 officially recognized languages. OCR in Gujarati has a number of problems, making it challenging to spot worldwide invariant forms and irregularities in handwritten Gujarati script. Another significant problem with handwritten Gujarati script is the shortage of an important comparison dataset. This problem was discovered, and a review was made. We created a dataset and treated it with invariant moments and used Gaussian and KNN Classifier for classification. KNN gave better results as compared to Gaussian.
Title: KNN and PCA Approach for Gujarati Script
Description:
Researchers are very interested in the essential pattern recognition used for offline handwriting recognition.
Any handwritten material may be converted into system-editable textual data by identifying concealed forms and understanding the typescripts included within the papers.
Gujarati is one of India's 22 officially recognized languages.
OCR in Gujarati has a number of problems, making it challenging to spot worldwide invariant forms and irregularities in handwritten Gujarati script.
Another significant problem with handwritten Gujarati script is the shortage of an important comparison dataset.
This problem was discovered, and a review was made.
We created a dataset and treated it with invariant moments and used Gaussian and KNN Classifier for classification.
KNN gave better results as compared to Gaussian.
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