Histogram of Oriented Gradients (HOG features) are used for feature extraction. Various machine learning algorithms, namely Decision Trees, Random Forest, Extra Trees Classifier, MLP, and SVM along with ensemble method were used for classification, and the accuracies compared.

Mar 1, 2025 · This paper aims to investigate the use of different machine learning models in Optical Character Recognition (OCR). Investigation of the performance of deep learning models, such as Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and their variations is performed.

This study presents a classification model using a hybrid classifier for the character recognition by combining holoentropy enabled decision tree (HDT) and deep neural network (DNN). In feature extraction, the local gradient features that

Optical character Recognition (OCR) is an important application of machine learning where an algorithm is trained on a data set of known letters/digits and can learn to accurately classify letters/digits.

In this paper, we present a new neural network (NN) based method for optical character recognition (OCR) as well as handwritten character recognition (HCR). Experimental results show that our proposed method

By carefully constructing effective probes, and assembling them into a geometric decision tree, we have devised, implemented, and compared a variety of methods to perform OCR.

Optical Character Recognition (OCR) is a process of converting scanned document into text document so it becomes editable and searchable. OCR is the mechanical or electronic translation of images of handwritten or printed text into machine-editable text.

Traditional machine learning algorithms, such as support vector machines (SVM) and decision trees, have been widely used for character recognition. However, deep learning techniques, especially convolutional neural networks (CNNs), have revolutionized OCR by enabling end-to-end learning models that

Oct 23, 2024 · Key advantages of using SVM in OCR include: High Accuracy: SVMs provide superior accuracy in character recognition, especially in complex and noisy image backgrounds. Effectiveness with Limited Data: Unlike deep learning models, SVMs can perform well with a smaller amount of training data.

Jan 1, 1996 · A new boosting algorithm of Freund and Schapire is used to improve the performance of an ensemble of decision trees which are constructed using the information ratio criterion of Quinlan's...