Sketch recognition

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

An interval approach to handwriting recognition

Abstract: A language is a set of symbols, signs, sounds, .... Each language has its own signs (Arab, Japanese, Latin....) that people discern easily. In fact, one gives these symbols (isolated or regrouped in a words or sentences) senses or meaning. It is a means of communication to describe our desires, our thought, our needs, .... Nevertheless, there are always basic references through which one communicates. These references are not fixed. They evolve with time, knowledge, experience.... Thus, machines "which understand human languages" have to recognize a given situation "in different sentences". This language can be written (handwritten recognition) or spoken (signal processing) or with gestures (computer vision) and grimaces (face recognition). In this paper, one is interested in the handwriting recognition which is one of the most difficult problems because of the diversity, and the no homogeneity of writing. In fact, to solve this problem, one is going to use the notion of references excessively.

Recognition of hand gestures on the video stream based on a statistical algorithm with pre-treatment

Abstract: The goal of this work is human hand detection and gesture recognition. This is a tremendously difficult task as hands can be very varied in shape and viewpoint, they can de open or closed, they can have different finger articulations. It is proposed a combined method of hand gesture recognition based on a statistical image processing algorithm. As a pre-processing algorithm it was applied Lucas-Kanade method and background subtraction algorithm. Object recognition was performed with using of Haar classifiers. The possibility of using gestures for remote control of various devices with different hands position from the camera location was shown.

Using systematic data flow with help of hand gesture based digit recognition

Abstract: Recognition of static hand gestures in our daily plays an important role in human-computer interaction. Hand gesture recognition has been a challenging task now days so a lot of research topic has been going on due to its increased demands in human computer interaction. Since Hand gestures have been the most natural communication medium among human being, so this facilitate efficient human computer interaction in many electronics gazettes . This has led us to take up this task of hand gesture recognition. In this paper different hand gestures are recognized and no of fingers are counted. Recognition process INVOLVES steps like feature extraction, features reduction and classification. To make the recognition process robust against varying illumination we used lighting compensation method along with YCbCr model.

Sketch Based Image Retrieval using Transfer Learning

Abstract: Sketch based image recognition is an approach where the system recognizes an image from the database based on the query sketch received from the user through an interface. In our implementation, we use transfer learning approach to learn the features of the sketch. Transfer learning is a technique used to reuse a model, which is pretrained, for a new problem thereby enhancing the model accuracy and reducing training time. Sketchy Database is used for training which contains 75,471 sketches of 125 categories. A pretrained VGG19 network is used to find the similarity between the sketch and image database using a cosine similarity function. Based on the result of the similarity function, SBIR recognizes a set of similar images corresponding to the sketch. Since the uses of touch devices are increasing rapidly even in a relatively small area, we focus on our final solution being scalable as well as distributed.

CASOOK: Creative Animating Sketchbook

Abstract: Understanding drawn picture information is an important task because drawings are one of the most intrinsic representations of individuals, regardless of age, national origin, or culture. However, it is difficult to define an appropriate picture model for computers. To address this challenge, we propose the Creative Animating Sketchbook (CASOOK) application and two basic modules through which the application is constructed. We first build a drawing system as the primary module. It includes a usable interface, which enables users to obtain various features of pictures through drawing, thereby encouraging users to draw. We then extract this primary module with a support vector machine classifier utilizing a binary decision tree. Two-layer sketch recognitions are produced; these can predict complex object classes in free drawing, which comprises the second proposed module. By combining the two proposed modules, we construct the CASOOK system. Based on results from user and computer experiments, we confirm the effectiveness of the proposed modules.