Sketch recognition

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

DCSR: A Digital Circuit Sketch Recognition System for Education

Abstract: Digital logic is an important part of any engineering curriculum in today’s digital era, and it is often taught visually through circuit diagrams. However, for students just learning logic, this process can be non-interactive, with students typically drawing and solving diagrams that will only be evaluated by a human grader later. This paper presents DCSR (Digital Circuit Sketch Recognition), a system that recognizes hand-drawn digital logic circuits through a web interface and calculates the truth value of its output based on students’ input. It allows users to draw freely and gives immediate feedback; DCSR aims to provide an interactive, sketch-based approach for educators to assist students in learning digital logic. It was evaluated by 15 electrical engineering students.

A two-level model-based object recognition technique

Abstract: Object recognition is the problem of detecting the presence and determining the pose of a set of known objects in given images. For some applications, the known objects may be composed of identical components. The relations among these components can be exploited to improve recognition accuracy. This paper introduces a two-level model for object recognition which reduces redundant work due to objects with identical components by explicitly specifying the components and the relations among them. Using automatic analysis of music scores as example, an empirical study is presented, demonstrating the effectiveness and properties of the technique. >

SKETRACK: Stroke-Based Recognition of Online Hand-Drawn Sketches of Arrow-Connected Diagrams and Digital Logic Circuit Diagrams

Abstract: Digitalization of handwritten documents has created a greater need for accurate online recognition of hand-drawn sketches. However, the online recognition of hand-drawn diagrams is an enduring challenge in human-computer interaction due to the complexity in extracting and recognizing the visual objects reliably from a continuous stroke stream. This paper focuses on the design and development of a new, efficient stroke-based online hand-drawn sketch recognition scheme named SKETRACK for hand-drawn arrow diagrams and digital logic circuit diagrams. The fundamental parts of this model are text separation, symbol segmentation, feature extraction, classification, and structural analysis. The proposed scheme utilizes the concepts of normalization and segmentation to isolate the text from the sketches. Then, the features are extracted to model different structural variations of the strokes that are categorized into the arrows/lines and the symbols for effective processing. The strokes are clustered using the spectral clustering algorithm based on p-distance and Euclidean distance to compute the similarity between the features and minimize the feature dimensionality by grouping similar features. Then, the symbol recognition is performed using modified support vector machine (MSVM) classifier in which a hybrid kernel function with a lion optimized tuning parameter of SVM is utilized. Structural analysis is performed with lion-based task optimization for recognizing the symbol candidates to form the final diagram representations. This proposed recognition model is suitable for simpler structures such as flowcharts, finite automata, and the logic circuit diagrams. Through the experiments, the performance of the proposed SKETRACK scheme is evaluated on three domains of databases and the results are compared with the state-of-the-art methods to validate its superior efficiency.

Face Sketch Recognition-An Overview

Abstract: Face Sketch recognition has been one of the most studied topics in Forensic literature. The automatic retrieval of suspect photos from the mug-shot police data-base can help them, quickly reduce and deduct potential suspects, but in most cases, the photographic image of a suspect is not available. The best substitute is often sketch based on the memory of an eyewitness or a victim. Generally, this process is slow and really not effective, it does not allow to find and arrest the right suspect. So, a stronger algorithm for even partial face sketch recognition can be useful. Although many methods have been proposed in this scenario, especially the techniques that are applied to face recognition system and ranked among the perfect and most effective. The main objective of this paper is to presents a review of recent and different researches on recognizing face sketches. We wrote this work to discover and analyze multiple researches on face sketch recognition. We wrote it to review the different classes of approaches, of datasets and evaluation protocols used, by analyzing their kernels and identifying their limitations.