Sketch recognition

About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.

A Semi-incremental Recognition Method for On-Line Handwritten English Text

Abstract: This paper presents a semi-incremental recognition method for online handwritten English text. We employ local processing strategy and focus on a recent sequence of strokes defined as "scope". For the latest scope, we build and update a segmentation and recognition candidate lattice and advance the best-path search incrementally. We utilize the result of the best-path search in the previous scope to exclude unnecessary segmentation candidates. This reduces the number of candidate word recognition with the result of reduced processing time. We also reuse the segmentation and recognition candidate lattice in the previous scope for the latest scope. Moreover, triggering recognition processes every few strokes save CPU time. Experiment made on IAM-OnDB database shows the effectiveness of the proposed method not only in reduced processing time and waiting time, but also in recognition accuracy.

Rata.gesture: A gesture recognizer developed using data mining

Abstract: Although many approaches to digital ink recognition have been proposed, most lack the flexibility and adaptability to provide acceptable recognition rates across a variety of problem spaces. This project uses a systematic approach of data mining analysis to build a gesture recognizer for sketched diagrams. A wide range of algorithms was tested, and those with the best performance were chosen for further tuning and analysis. Our resulting recognizer, RATA.Gesture, is an ensemble of four algorithms. We evaluated it against four popular gesture recognizers with three data sets; one of our own and two from other projects. Except for recognizer-data set pairs (e.g., PaleoSketch recognizer and PaleoSketch data set) the results show that it outperforms the other recognizers. This demonstrates the potential of this approach to produce flexible and accurate recognizers.

Anatomy on pattern recognition

Abstract: Pattern Recognition is the science of recognizing patterns by machines. This is very wide research area as of today, because every new research tries to make machine as intelligent as human for recognizing patterns. Pattern recognition is an active research and an important trait of ‘artificial intelligence’. This review paper introduces pattern recognition, its fundamental definitions, and provides understanding of related research work. This paper presents different types of algorithms, their limitations & applications of pattern recognition.

Shape recognition: a landmark-based approach

Abstract: Shape recognition has applications in computer vision tasks such as industrial automated inspection and automatic target recognition. When objects are occluded, many recognition methods that use global information will fail. To recognize partially occluded objects, we represent each object by a Set of landmarks. The landmarks of an object are points of interest which have important shape attributes and are usually obtained from the object boundary. In this study, we use high curvature points along an object boundary as the landmarks of the object. Given a scene consisting of partially occluded objects, the hypothesis of a model object in the scene is verified by matching the landmarks of an object with those in the scene. A measure of similarity between two landmarks, one from a model and the other from a scene, is needed to perform this matching. One such local shape measure is the sphericity of a triangular transformation mapping the model landmark and its two neighboring landmarks to the scene landmark and its two neigh­ boring landmarks. Sphericity is in general defined for a diffeomorphism. Its invariant properties under a group of transformation, namely, translation, rotation, and scaling are derived. The sphericity of a triangular transformation is shown to be a robust local shape measure in the sense that minor distortion in the

Hand Gesture Recognition Based on HOG Characters and SVM

Abstract: Hand gesture recognition has abundant application fields,and the change of fields will result great influence to the result of recognitionIn all gesture recognition methods,the one which based on computer vision is most sensitive to environment,such as too bright or too dark light,complex background,hand gesture rotation,etcIn order to overcome the impact of environment,histograms of oriented gradient which is being used in target detection research widely is used in hand gesture recognitionThis method makes hand gesture recognition based on computer vision be sensitive to environment no longer,and a good recognition effect has been achieved