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Sketch recognition

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


Papers
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Proceedings ArticleDOI
07 Jun 2010
TL;DR: This work provides a method for extracting and classifying stroke segments from a line drawing or sketch with the goal of producing perceptually-valid output in the context of mesh inflation.
Abstract: We provide a method for extracting and classifying stroke segments from a line drawing or sketch with the goal of producing perceptually-valid output in the context of mesh inflation. This is important as processing freehand sketch input is a fundamental task in sketch-based interfaces, yet many systems bypass the problem by forcing simplified, unnatural drawing patterns. Our stroke extraction combines contour tracing with feature-preserving post-processing. The extracted strokes are classified according to the objects and regions in the sketch: object and region boundaries, interior features, and suggestive lines. The outcome of this classification is demonstrated with examples in feature-sensitive mesh inflation.

13 citations

Book ChapterDOI
18 Aug 2005
TL;DR: This paper presents a novel approach for adaptive online multi-stroke sketch recognition based on Hidden Markov Model that views the drawing sketch as the result of a stochastic process that is governed by a hidden Stochastic model and identified according to its probability of generating the output.
Abstract: This paper presents a novel approach for adaptive online multi-stroke sketch recognition based on Hidden Markov Model (HMM). The method views the drawing sketch as the result of a stochastic process that is governed by a hidden stochastic model and identified according to its probability of generating the output. To capture a user’s drawing habits, a composite feature combining both geometric and dynamic characteristics of sketching is defined for sketch representation. To implement the stochastic process of online multi-stroke sketch recognition, multi-stroke sketching is modeled as an HMM chain while the strokes are mapped as different HMM states. To fit the requirement of adaptive online sketch recognition, a variable state-number determining method for HMM is also proposed. The experiments prove both the effectiveness and efficiency of the proposed method.

13 citations

Proceedings ArticleDOI
10 Nov 2003
TL;DR: The performance of Fourier descriptors and Hu's seven moment invariants for an Optical Character Recognition (OCR) engine developed for 3D model-based object recognition applications is discussed.
Abstract: This paper discusses the performance of Fourier descriptors and Hu's seven moment invariants for an Optical Character Recognition (OCR) engine developed for 3D model-based object recognition applications.

12 citations

Proceedings ArticleDOI
01 Aug 2009
TL;DR: A novel technique for creating a large and varied ground-truthed corpus for hand drawn math recognition via random walks through a context-free grammar, and an algorithm automatically generates ground-truth data for individual symbols and inter-symbol relationships within the math expressions.
Abstract: In sketch recognition systems, ground-truth data sets serve to both train and test recognition algorithms. Unfortunately, generating data sets that are sufficiently large and varied is frequently a costly and time-consuming endeavour. In this paper, we present a novel technique for creating a large and varied ground-truthed corpus for hand drawn math recognition. Candidate math expressions for the corpus are generated via random walks through a context-free grammar, the expressions are transcribed by human writers, and an algorithm automatically generates ground-truth data for individual symbols and inter-symbol relationships within the math expressions. While the techniques we develop in this paper are illustrated through the creation of a ground-truthed corpus of mathematical expressions, they are applicable to any sketching domain that can be described by a formal grammar.

12 citations

Journal ArticleDOI
TL;DR: A user-drawn sketch image-based three-dimensional object recognition method, which automatically learns and optimizes features by using unsupervised algorithm to overcome the difficulty of extracting robust features from the black and white sketch image, which performs favorably against several state-of-the-art algorithms.

12 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202326
202271
202130
202029
201946
201827