Enhancing Document Image Retrieval in Education: Leveraging Ensemble-Based Document Image Retrieval Systems for Improved Precision

Document image retrieval (DIR) systems simplify access to digital data within printed documents by capturing images. These systems act as bridges between print and digital realms, with demand in organizations handling both formats. In education, students use DIR to access online materials, clarify topics, and find solutions in printed textbooks by photographing content with their phones. DIR excels in handling complex figures and formulas. We propose using ensembles of DIR systems instead of single-feature models to enhance DIR’s efficacy. We introduce “Vote-Based DIR” and “The Strong Decision-Based DIR”. These ensembles combine various techniques, like optical code reading, spatial analysis, and image features, improving document retrieval. Our study, using a dataset of university exam preparation materials, shows that ensemble DIR systems outperform individual ones, promising better accuracy and efficiency in digitizing printed content, which is especially beneficial in education.

[1]  A. Topcu,et al.  Social Media Zero-Day Attack Detection Using TensorFlow , 2023, Electronics.

[2]  A. Topcu,et al.  Machine Learning-Based Text Classification Comparison: Turkish Language Context , 2023, Applied Sciences.

[3]  C. Suen,et al.  End-to-end learning of representations for instance-level document image retrieval , 2023, Appl. Soft Comput..

[4]  Khawaja Tehseen Ahmed,et al.  Deep learned vectors’ formation using auto-correlation, scaling, and derivations with CNN for complex and huge image retrieval , 2022, Complex & Intelligent Systems.

[5]  Shubham Agrawal,et al.  Content-based medical image retrieval system for lung diseases using deep CNNs , 2022, International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management.

[6]  Da Cao,et al.  Keyword-Based Diverse Image Retrieval With Variational Multiple Instance Graph , 2022, IEEE Transactions on Neural Networks and Learning Systems.

[7]  A. Neelima,et al.  Efficient content-based image retrieval using deep search and rescue algorithm , 2022, Soft Computing.

[8]  W. Wong,et al.  Concentrated hashing with neighborhood embedding for image retrieval and classification , 2022, International Journal of Machine Learning and Cybernetics.

[9]  A. J.,et al.  A faster secure content-based image retrieval using clustering for cloud , 2021, Expert Syst. Appl..

[10]  Gaurav Dhiman,et al.  A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants , 2020, Neural Computing and Applications.

[11]  M. S. Shirdhonkar,et al.  Language-based document image retrieval for Trilingual System , 2019, International Journal of Information Technology.

[12]  Yu Wang,et al.  A decisive content based image retrieval approach for feature fusion in visual and textual images , 2019, Knowl. Based Syst..

[13]  V. K. Govindan,et al.  Content-based medical image retrieval by spatial matching of visual words , 2018, J. King Saud Univ. Comput. Inf. Sci..

[14]  Shahzad Qaiser,et al.  Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents , 2018, International Journal of Computer Applications.

[15]  Alireza Alaei,et al.  Logo and seal based administrative document image retrieval: A survey , 2016, Comput. Sci. Rev..

[16]  Santanu Chaudhury,et al.  Camera-based document image matching using multi-feature probabilistic information fusion , 2015, Pattern Recognit. Lett..

[17]  David G. Lowe,et al.  Scalable Nearest Neighbor Algorithms for High Dimensional Data , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  F. Perronnin,et al.  Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Ernest Valveny,et al.  Large-scale document image retrieval and classification with runlength histograms and binary embeddings , 2013, Pattern Recognit..

[20]  Jean-Philippe Domenger,et al.  Semi-structured document image matching and recognition , 2013, Electronic Imaging.

[21]  Kaspar Riesen,et al.  Recent advances in graph-based pattern recognition with applications in document analysis , 2011, Pattern Recognit..

[22]  Ehsanollah Kabir,et al.  Binarization of degraded document image based on feature space partitioning and classification , 2010, International Journal on Document Analysis and Recognition (IJDAR).

[23]  Nikos Papamarkos,et al.  A Document Image Retrieval System , 2010, Eng. Appl. Artif. Intell..

[24]  Azriel Rosenfeld,et al.  Classification of document pages using structure-based features , 2001, International Journal on Document Analysis and Recognition.

[25]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[26]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Mohamad Mahmoud Al Rahhal,et al.  Multilanguage Transformer for Improved Text to Remote Sensing Image Retrieval , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[29]  Konstantinos G. Derpanis,et al.  Overview of the RANSAC Algorithm , 2005 .