Investigation of Machine Learning Algorithms for Pattern Recognition in Image Processing

In order to recognize patterns in images, this study tests the performance of many "machine learning algorithms" and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ("deep learning, supp...

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Veröffentlicht in:2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA) S. 898 - 904
Hauptverfasser: Kate, Chennaiah, Kalpana, C., Sharma, Arvind, Yadav, Ajay Singh, Kumar, Ashok, Kumar, S. Sandeep
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 03.08.2023
Schlagworte:
"decision trees"
"random forests"
"support vector machines"
computer vision
Deep learning
evaluation metrics
feature extraction
Forestry
Handwriting recognition
Histogram of Oriented Gradients
Histograms
image processing
Image recognition
Machine learning
Machine learning algorithms
pattern recognition
raw pixel values
synthetic images
transfer learning
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Zusammenfassung:In order to recognize patterns in images, this study tests the performance of many "machine learning algorithms" and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ("deep learning, support vector machines, decision trees, and random forests") and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its "accuracy, precision, recall, and F1 score", among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms.
DOI:10.1109/ICIRCA57980.2023.10220656