Most sketch recognition systems are accurate in recognizing either text or shape (graphic) ink strokes, but not both. Distinguishing between shape and text strokes is, therefore, a critical task in recognizing hand drawn digital ink diagrams which commonly contain many text labels and annotations. We have found the ‘entropy rate’ to be an accurate criterion of classification. We found that the entropy rate is significantly higher for text strokes compared to shape strokes and can serve as a distinguishing factor between the two. Using entropy values, our system produced a correct classification rate of 92.06% on test data belonging to diagrammatic domain for which the threshold was trained on. It also performed favorably on data for which no training examples at all were supplied.
In this work, we target at the problem of offline sketch parsing, in which the temporal orders of strokes are unavailable. It is more challenging than most of existing work, which usually leverages the temporal information to reduce the search space. Different from traditional approaches in which thousands of candidate groups are selected for recognition, we propose the idea of shapeness estimation to greatly reduce this number in a very fast way. Based on the observation that most of hand-drawn shapes with well-defined closed boundaries can be clearly differentiated from non-shapes if normalized into a very small size, we propose an efficient shapeness estimation method. A compact feature representation as well as its efficient extraction method is also proposed to speed up this process. Based on the proposed shapeness estimation, we present a three-stage cascade framework for offline sketch parsing. The shapeness estimation technique in this framework greatly reduces the number of false positives, resulting in a 96.2% detection rate with only 32 candidate group proposals, which is two orders of magnitude less than existing methods. Extensive experiments show the superiority of the proposed framework over state-of-the-art works on sketch parsing in both effectiveness and efficiency, even though they leveraged the temporal information of strokes.
Objects in freely-drawn sketches often have no spatial or temporal separation, making object recognition difficult. We present a two-step stroke-grouping algorithm that first classifies individual strokes according to the type of object to which they belong, then groups strokes with like classifications into clusters representing individual objects. The first step facilitates clustering by naturally separating the strokes, and both steps fluidly integrate spatial and temporal information. Our approach to grouping is unique in its formulation as an efficient classification task rather than, for example, an expensive search task. Our single-stroke classifier performs at least as well as existing single-stroke classifiers on text vs. nontext classification, and we present the first three-way single-stroke classification results. Our stroke grouping results are the first reported of their kind; our grouping algorithm correctly groups between 86% and 91% of the ink in diagrams from two domains, with between 69% and 79% of shapes being perfectly clustered.
Unlike English, where unfamiliar words can be queried for its meaning by typing out its letters, the analogous operation in Chinese is far from trivial due to the nature of its written language. One approach for querying Chinese characters involve referencing their dictionary component called radicals. This is advantageous since users would not need to know their pronunciation nor their stroke-order, a requirement in other querying approaches. Currently though, sketching a character's radical for querying is an unsupported capability in existing systems. Using the geometric-based LADDER sketching language combined with the Sezgin lowlevel recognizer, we were able to construct an application which can first recognize handwritten sketches of Chinese radical, and then output candidate Chinese characters which contain that radical. Thus, we were able to demonstrate that a geometric-based sketch recognition approach can be used to easily build applications for recognizing symbols related to Chinese characters while having reasonable recognition rates. Unlike current image-based recognition systems, our system also maintains stroke order information of characters. Since stroke order is important in written Chinese, our system can be easily expanded for use in Chinese language education by providing visual feedback to students on correct stroke order.