Sketch recognition

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

Arrow R-CNN for Flowchart Recognition

Abstract: We propose Arrow R-CNN for recognizing the symbols and structure of offline handwritten flowcharts. Arrow R-CNN extends the Faster R-CNN object detection system with an arrow keypoint predictor. This keypoint predictor is used to reconstruct the flowchart structure. We propose a network architecture and data augmentation methods that allow us to train a very deep model on a small publicly available flowchart dataset. Evaluation results show that Arrow R-CNN outperforms existing offline systems by a wide margin. For comparison with existing online flowchart recognizers, we propose an extension for mapping strokes to recognized symbols. Results show that Arrow R-CNN also achieves state of the art in online recognition, even though it does not explicitly leverage stroke information. An ablation study reveals that data augmentation guided by domain knowledge is key to high accuracy on such a small dataset.

Generic target recognition

Abstract: We present in this paper a study on target recognition. The goal of this work is to determine and compare different methods from the pattern recognition domain in order to be able to recognize some objects in an image. We suppose having detected by a segmentation process a candidate object appearing with an unknown scale or rotation. To be able to recognize this object, we have first to describe it by some features having the property to be invariant by rotation, translation or scale. Second, we have to realize a supervised classification in order to compare this unknown object with one from the knowledge database. We present some experimental results for target recognition by comparing several features, classification methods and methodologies.

Multiple Handwritten Text Line Recognition Systems Derived from Specific Integration of a Language Model

Abstract: This paper investigates the generation and use of multiple recognition results to improve the performance of an offline handwritten text line recognition system. Multiple recognition results are created by specific integration of a language model in the hidden Markov model based recognition system. The ROVER algorithm is applied to combine the multiple results. Experiments conducted on the IAM database show that the proposed system is able to produce statistically significant improvements in the recognition rate compared to the original system.

SVM-based sketch recognition: which hyperparameter interval to try?

Abstract: Hyperparameters are among the most crucial factors that affect the performance of machine learning algorithms. In general, there is no direct method for determining a set of satisfactory parameters, so hyperparameter search needs to be conducted each time a model is to be trained. In this work, we analyze how similar hyperparameters perform across various datasets from the sketch recognition domain. Results show that hyperparameter search space can be reduced to a subspace despite differences in characteristics of datasets.

A dynamic hand gesture recognition system for controlling VLC media player

Abstract: In this paper we have discussed a low cost system which uses dynamic hand gesture recognition technique to control the VLC media player. This application contains a central computation module which segments the foreground part of the frame using skin detection and approximate median technique. The recognition of gesture is done by creating a Decision Tree, that uses various features extracted from the segmented part. This hand gesture recognition technique introduces a new, natural way to interact with computers.