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Published Paper Details:
Paper Title
MULTI-LANGUAGE DIGIT CLASSIFICATION USING RANDOM FOREST CLASSIFIER
Authors
Gajendra Sharma, Jaswanth, Hemanath Reddy, Hithesh
Keywords
Sklearn, Random Forest, HOG and NumPy.
Abstract
Handwritten digit classification is the system's ability to predict and detect handwritten digits. Now a days machine learning has more significant role in classification problems. The created model is Random Forest classifier. The dataset used for the project is prepared by own where each dataset consists of 2000 samples of Telugu, English and Hindi language digit images. The aim of the paper is to observe the working of random forest classifier created by using Sklearn python package in terms of training accuracy, testing accuracy and confusion matrix. The Multilanguage refers to combination of Telugu, English and Hindi language digits into a single dataset. The testing accuracy is found to be 99% for 50 decision trees.
IJCRT's Publication Details
Unique Identification Number - IJCRT22A6604
Paper ID - 222062
Page Number(s) - e951-e955
Pubished in - Volume 10 | Issue 6 | June 2022
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Gajendra Sharma, Jaswanth, Hemanath Reddy, Hithesh, "MULTI-LANGUAGE DIGIT CLASSIFICATION USING RANDOM FOREST CLASSIFIER", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 6, pp.e951-e955, June 2022, Available at :http://www.ijcrt.org/papers/IJCRT22A6604.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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