Historical Manuscript Production Date Estimation Using Deep Convolutional Neural Networks

Wahlberg, Fredrik; Wilkinson, Tomas; Brun, Anders

doi:10.1109/icfhr.2016.0048

Cited by 21 publications

(14 citation statements)

References 11 publications

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“…1) Classification: This task is well known in the computer vision community and consists of producing one or more descriptive labels for a given input image. In the context of historical image document analysis this task can be, for example, formulated as character recognition [21], [22], style/script classification [23], [24] or manuscript dating [24], [25]. We train the networks to minimize the cross-entropy loss function shown below:…”

Section: A Tasksmentioning

confidence: 99%

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Studer

Alberti

Pondenkandath

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of humanannotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pretraining for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval.

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Section: A Tasksmentioning

confidence: 99%

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Studer

Alberti

Pondenkandath

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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“…RNN can also be used for event date estimation [72] and many other purposes. As we have seen, sequence models have been increasingly applied in agriculture, even though the main deep learning methods used in agricultural tasks are still the CNN-based models.…”

Section: Rnn Applications In Smart Agriculturementioning

confidence: 99%

Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Zhu¹,

Liu²,

Liu³

et al. 2018

International Journal of Agricultural and Biological Engineering

141

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In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively.

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“…AlexNet [25] is one of the important mile stones in the development of deep learning models, which is based on CNN. The CNN has also been used in document analysis [26][27][28]. The CNN application in the area of writer recognition is gaining popularity [29][30][31][32].…”

Section: Vinita Balbhim Patil Rajendra R Patilmentioning

confidence: 99%

Writer Identification with Hybrid Model using Hybrid HMM and ANN

Patil*¹,

Patil²

2019

IJRTE

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In this paper, writer identification is performed with three models, namely, HMMBW, HMMMLP and HMMCNN. The features are extracted from the HMM and are classified using Baum Welch algorithm (BW), Multi layer perceptron (MLP) model and Convolutional neural network (CNN) model. A dataset, namely, VTU-WRITER dataset is created for the experiential purpose and the performance of the models were tested. The test train ratio was varied to derive its relation to accuracy. Also the number of states was varied to determine the optimum number of states to be considered in the HMM model. Finally the performance of all the three models is compared

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Historical Manuscript Production Date Estimation Using Deep Convolutional Neural Networks

Cited by 21 publications

References 11 publications

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Writer Identification with Hybrid Model using Hybrid HMM and ANN

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