Histograms of Points, Orientations, and Dynamics of Orientations Features for Hindi Online Handwritten Character Recognition
A set of features independent of character stroke direction and order variations is proposed for online handwritten character recognition. A method is developed that maps features like co-ordinates of points, orientations of strokes at points, and dynamics of orientations of strokes at points spatially as a function of co-ordinate values of the points and computes histograms of these features from different regions in the spatial map. Different features like spatio-temporal, discrete Fourier transform, discrete cosine transform, discrete wavelet transform, spatial, and histograms of oriented gradients used in other studies for training classifiers for character recognition are considered. The classifier chosen for classification performance comparison, when trained with different features, is support vector machines (SVM). The character datasets used for training and testing the classifiers consist of online handwritten samples of 96 different Hindi characters. There are 12832 and 2821 samples in training and testing datasets, respectively. SVM classifiers trained with the proposed features has the highest classification accuracy of 92.9\% when compared to the performances of SVM classifiers trained with the other features and tested on the same testing dataset. Therefore, the proposed features have better character discriminative capability than the other features considered for comparison.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Classifier Using Global Character Level and Local Sub-unit Level Features for Hindi Online Handwritten Character Recognition
A classifier is developed that defines a joint distribution of global character features, number of sub-units and local sub-unit features to model Hindi online handwritten characters. The classifier uses latent variables…
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood. We present evidence that DNNs are capable of generalizing to object…
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
Recently, 3D understanding research sheds light on extracting features from point cloud directly, which requires effective shape pattern description of point clouds. Inspired by the outstanding 2D shape descriptor SIFT, …
Point Cloud SegmentationSemantic SegmentationContent-based image retrieval using Mix histogram
This paper presents a new method to extract image low-level features, namely mix histogram (MH), for content-based image retrieval. Since color and edge orientation features are important visual information which help th…
Content-Based Image RetrievalImage RetrievalRetrievalOriented RepPoints for Aerial Object Detection
In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary orientations having the cluttered surroundings. Unlike the mainstreamed approaches regressing the bounding box orientations, thi…
ObjectObject DetectionObject Detection In Aerial ImagesOne-stage Anchor-free Oriented Object Detection+1