Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words

Abstract

In this paper we propose the use of several feature extraction methods, which have been shown before to perform well for object recognition, for recognizing handwritten characters. These methods are the histogram of oriented gradients (HOG), a bag of visual words using pixel intensity information (BOW), and a bag of visual words using extracted HOG features (HOG-BOW). These feature extraction algorithms are compared to other well-known techniques: principal component analysis, the discrete cosine transform, and the direct use of pixel intensities. The extracted features are given to three different types of support vector machines for classification, namely a linear SVM, an SVM with the RBF kernel, and a linear SVM using L2-regularization. We have evaluated the six different feature descriptors and three SVM classifiers on three different handwritten character datasets: Bangla, Odia and MNIST. The results show that the HOG-BOW, BOW and HOG method significantly outperform the other methods. The HOG-BOW method performs best with the L2-regularized SVM and obtains very high recognition accuracies on all three datasets.

Comparative study between deep learning and bag of visual words for wild-animal recognition

“…The results also show that the BOW method using color information with the max-pooling strategy outperforms the HOG-BOW methods for both gray and color image information on our dataset for both spatial pooling strategies. This is contrary to the view that HOG-BOW techniques outperform BOW methods, which was shown before in character recognition [16] and facial recognition [17].…”

“…Some recent works have used BOW as an input to some hierarchical structures such as weakly supervised deep metric learning [14] and robust structured subspace learning [15]. Moreover, the combination of BOW with the histogram of oriented gradients on grayscale datasets has obtained a very good performance on both handwritten character recognition [16] and face recognition [17]. In [18], the authors applied BOW on text detection and character recognition on scene images.…”

Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition

“…The HOG feature extractor represents objects by counting occurrences of gradient intensities and orientations in localized portions of an image. Based on the work of (Bertozzi et al, 2007;Surinta et al, 2015), the HOG descriptor computes feature vectors using the following steps: Figure 2: The illustration of the GoogleNet architecture (Szegedy et al, 2015). All convolutional layers and inception modules have a depth of two.…”

“…In our experiments, based on the work of Surinta et al (2015), the HOG descriptor is employed as the local descriptor. The number of patches is set to 400,000, the size of each patch is 15 × 15 pixels, and the number of centroids is set to 600.…”

Plant recognition, detection, and counting with deep learning

“…The HOG feature extractor represents objects by counting occurrences of gradient intensities and orientations in localized portions of an image. Based on the work of (Bertozzi et al, 2007;Surinta et al, 2015), the HOG descriptor computes feature vectors using the following steps:…”