Pen ink discrimination in handwritten documents using statistical and motif texture analysis : A classification based approach

Dansena, Prabhat; Bag, Soumen; Pal, Rajarshi

doi:10.1007/s11042-022-12843-x

Cited by 4 publications

(2 citation statements)

References 49 publications

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“…However, a real challenge would be to detect handwritten essays in open-text type questions and interpret text meaning, which would require the usage of deep neural network models [14,15] and natural language processing techniques [5]. Furthermore, if the software could someday verify the integrity of the text handwritten by the pen [8,24], that would significantly contribute to preserving the examination process's soundness. That is something that we will strive for in the future.…”

Section: Discussionmentioning

confidence: 99%

Automated assessment of pen and paper tests using computer vision

Jocovic,

Marinkovic,

Stojanovic

et al. 2023

Multimed Tools Appl

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Computer vision is one of the artificial intelligence’s most challenging fields, enabling computers to interpret, analyse and derive meaningful information from the visual world. There are various utilizations of computer vision algorithms, and most of them, from simpler to more complicated, have an object and shape recognition in common. Traditional pen and paper tests are designed in a pre-established format and consist of numerous basic shapes, which designate the important parts of the test itself. With that in mind, many computer vision applications regarding pen and paper tests arise as an opportunity. Massive courses and large schooling organizations mostly conduct their exams in paper format and assess them manually, which imposes a significant burden on the teaching staff. Any kind of automatization that will facilitate the grading process is highly desirable. Hence, an automated answer recognition system in assessment was developed to mitigate the problems above. The system uses images of scanned test pages obtained from the test scanning process and performs the necessary image manipulation steps to increase target recognition accuracy. Further, it manages to identify regions of interest containing multiple-choice questions and contours. Finally, the system verifies obtained results using the knowledge of the whereabouts of the test template regions of interest.

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Section: Discussionmentioning

confidence: 99%

Automated assessment of pen and paper tests using computer vision

Jocovic,

Marinkovic,

Stojanovic

et al. 2023

Multimed Tools Appl

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“…Through the study of news text classification algorithms, it was found that traditional machine learning methods are prone to losing useful semantic feature information in the process of text representation, while using models such as Word2Vec and glove for text representation and then sharing text contextual semantic information by training neural network models can learn more vector representations as features, which is significantly better than traditional machine in terms of classification accuracy learning methods [7][8]. However, models such as Word2Vec cannot solve the problem of multiple meanings of words, especially in the face of the sparse features and context-dependent nature of news headlines, and there are still many semantic problems to be solved [9][10]. At present, the idea of deep neural network-based news text classification is to have deep networks automatically complete the extraction of features for efficient and accurate classification, and models such as TextCNN and LSTM are generally used [11][12].…”

Section: Key Issues In News Text Classificationmentioning

confidence: 99%

Machine Learning Based News Text Classification

2020

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With the rapid development of the Internet era, the explosive growth of news data volume and the lack of effective management are gradually becoming serious problems, and it is increasingly difficult for readers to obtain valuable information quickly. How to quickly search for valuable information from the large amount of news text information is a meaningful task in text classification. Existing research methods still have some problems, such as directly combining the headline with the text, thus neglecting the importance of the headline, and the single model of classification, which leads to low classification results. For this reason, the main objective of this paper is to investigate the classification of news texts based on machine learning. This paper examines the current state of deep learning-based text classification and, in combination with the characteristics of news texts, chooses a machine learning-based text classification method to further explore and study news texts. Through the response time and core algorithm accuracy test of this system, the system better reflects the excellent performance of the system and meets the actual performance requirements of the system. The proposed news system can not only process system requests quickly, but also has excellent accuracy rate, which can better assist users to filter information and improve the user's experience of reading news.

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