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Document Image Classification with Vision Transformers
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, 2022© 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.Document image classification has received huge interest in business automation processes. Therefore, document image classification plays an important role in the document image processing (DIP) systems.
Semih Sevim +2 more
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Document image classification: Progress over two decades
Neurocomputing, 2021Abstract Document image classification plays a vital role in the document image processing system. Thus it is of great importance to have a clear understanding of the state-of-the-art of the document image classification field, especially in this deep learning era, which will facilitate the development of effective document image processing systems ...
Qiu Chen, Taorong Qiu
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Hidden tree markov models for document image classification
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003Classification is an important problem in image document processing and is often a preliminary step toward recognition, understanding, and information extraction. In this paper, the problem is formulated in the framework of concept learning and each category corresponds to the set of image documents with similar physical structure.
P Frasconi, M Gori
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Cross-Modal Deep Networks For Document Image Classification
2020 IEEE International Conference on Image Processing (ICIP), 2020As a fundamental step of document related tasks, document classification has been widely adopted to various document image processing applications. Unlike the general image classification problem in the computer vision field, text document images contain both the visual cues and the corresponding text within the image.
Mickael Coustaty +2 more
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Highlighted Document Image Classification
Color and Imaging Conference, 2021Yafei Mao +6 more
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Multimodal Document Image Classification
2019 International Conference on Document Analysis and Recognition (ICDAR), 2019State-of-the-art methods for document image classification rely on visual features extracted by deep convolutional neural networks (CNNs). These methods do not utilize rich semantic information present in the text of the document, which can be extracted using Optical Character Recognition (OCR).
Rajiv Jain, Curtis Wigington
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Document image layout comparison and classification
Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), 1999The paper describes features and methods for document image comparison and classification at the spatial layout level. The methods are useful for visual similarity based document retrieval as well as fast algorithms for initial document type classification without OCR.
Jianying Hu +2 more
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Two Level Document Image Classification
2021 6th International Conference on Computer Science and Engineering (UBMK), 2021Classifying documents is an important process for organizations that are responsible for keeping a large number of documents in a digital archive. In this paper, a two-level method was used to classify approximately 253 class of documents. In the first stage, the documents were visually classified.
Adnan Oncevarlik +3 more
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Footnote-Based Document Image Classification
2017Analyzing historical document images is considered a challenging task due to the complex and unusual structures of these images. It is even more challenging to automatically find the footnotes in them. In fact, detecting footnotes is one of the essential elements for scholars to analyze and answer key questions in the historical documents. In this work,
Sara Zhalehpour +3 more
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Structural similarity for document image classification and retrieval
Pattern Recognition Letters, 2014Abstract This paper presents a novel approach to defining document image structural similarity for the applications of classification and retrieval. We first build a codebook of SURF descriptors extracted from a set of representative training images.
Jayant Kumar +2 more
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