Showing papers by "Gaurav Harit published in 2020"
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TL;DR: In this article, the authors proposed a new action scoring system as a two-phase system: (1) a Deep Metric Learning Module that learns similarity between any two action videos based on their ground truth scores given by the judges; (2) Score Estimation Module that uses the first module to find the resemblance of a video to a reference video in order to give the assessment score.
Abstract: Automated vision-based score estimation models can be used as an alternate opinion to avoid judgment bias. In the past works the score estimation models were learned by regressing the video representations to the ground truth score provided by the judges. However such regression-based solutions lack interpretability in terms of giving reasons for the awarded score. One solution to make the scores more explicable is to compare the given action video with a reference video. This would capture the temporal variations w.r.t. the reference video and map those variations to the final score. In this work, we propose a new action scoring system as a two-phase system: (1) A Deep Metric Learning Module that learns similarity between any two action videos based on their ground truth scores given by the judges; (2) A Score Estimation Module that uses the first module to find the resemblance of a video to a reference video in order to give the assessment score. The proposed scoring model has been tested for Olympics Diving and Gymnastic vaults and the model outperforms the existing state-of-the-art scoring models.
4 citations
TL;DR: This paper presents a novel problem of handwritten word spotting in cluttered environment where a word is cluttered by a strike-through with a line stroke, which is the combinatorics Vertical Projection Profile (cVPP) feature extracted and aligned by modified Dynamic Time Warping (DTW) algorithm.
Abstract: In this paper, we present a novel problem of handwritten word spotting in cluttered environment where a word is cluttered by a strike-through with a line stroke. These line strokes can be straight, slant, broken, continuous, or wavy in nature. Vertical Projection Profile (VPP) feature and its modified version, which is the combinatorics Vertical Projection Profile (cVPP) feature is extracted and aligned by modified Dynamic Time Warping (DTW) algorithm. The dataset for the proposed problem is not available so we prepared our dataset. We compare our method with Rath and Manmath [6], and PHOCNET [17] for handwritten word spotting in the presence of strike-through, and achieve better results.
1 citations
TL;DR: The effectiveness of horizontal-scale correction is proved by applying it as a preprocessing step in a recognition system proposed in (Almazan et al. in Pattern Anal Mach Intell 36(12):21552–2566, 2014 [2]).
Abstract: Preprocessing techniques form an important task in document image analysis. In structured documents like forms, cheques, etc., there is a predefined space called frame field/cell for the user to fill the entry. When the user is writing, the nonuniformity of inter-character spacing becomes an issue. Many times, the starting characters of the word are written with sparse spacing between the characters and then gradually with a more compact spacing so as to accommodate the word within the frame field. To deal with this variation in intra-word spacing, horizontal-scale correction is applied to the extracted form fields. The effectiveness of the system is proved by applying it as a preprocessing step in a recognition system proposed in (Almazan et al. in Pattern Anal Mach Intell 36(12):21552–2566, 2014 [2]). The recognition framework results in reduced error rates with this normalization.
TL;DR: In this article, a two-dimensional, stochastic context-free grammar is used for the structural analysis of offline handwritten mathematical expressions in a document image and the spatial relation between characters in an expression has been incorporated so that the structural variability in handwritten expressions can be tackled.
Abstract: Structural analysis helps in parsing the mathematical expressions. Various approaches for structural analysis have been reported in literature, but they mainly deal with online and printed expressions. In this work, two-dimensional, stochastic context-free grammar is used for the structural analysis of offline handwritten mathematical expressions in a document image. The spatial relation between characters in an expression has been incorporated so that the structural variability in handwritten expressions can be tackled.