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With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning classical models are highly dependent on manual se...
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Convolutional neural networks have shown successful results in image classification achieving real-time results superior to the human level. However, texture images still pose some challenge to these models due, for example, to the limited availability of data for training in several problems where these images appear, high inter-class similarity,...
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... Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: ...
Hand-drawn diagrams have been a standard visual communication tool in many disciplines, including architectural design, engineering, and education. The inherent diversity and absence of standardized formats of hand-drawn diagrams make it difficult to recognize them. As a result, there is an increasing need for efficient strategies and approaches for correctly identifying and interpreting hand-drawn diagrams. This research study comprehensively reviews hand-drawn diagram recognition (HDDR) techniques, emphasizing their importance and usefulness in numerous sectors. For the past ten years, articles from the Scopus database on HDDR have been extracted and reviewed. The study explores the approaches, steps, and benchmark datasets available to recognize hand-drawn diagrams. An attempt is made to get insights into the most recent state-of-the-art methodologies, their limits, and potential future advancement directions. This paper also suggests probable solutions to overcome the limitations and develop new techniques for efficiently and robustly recognizing hand-drawn diagrams.
Evaluation is a crucial issue in a learning system. Instructors frequently assign a collection of questions, which students must respond to in the script, in order to evaluate their performance. An answer is most often composed of text, equations, and figures. The sketched figures must be recognized and rated according to their actual appearance. With the advancement of computer vision, several methods have been developed for recognizing and grading handwritten text accurately. To ensure a fair automatic evaluation system, we must develop a system that can grade text and images simultaneously. Due to the complex structure of images, we need to extract important features in the image, unlike traditional text grading methods. The major focus of this research work is mostly on the freehand sketch phase, therefore developing a CNN model, that can classify and assign a grade to a given image automatically. The model is trained with a multi-labeled dataset where images are graded and labeled by the expert human evaluator. This dataset needed to undergo some preprocessing steps before being fed by the proposed CNN model.
