ARDIS: a Swedish historical handwritten digit dataset

Kusetoğulları, Hüseyin; Yavariabdi, Amir; Cheddad, Abbas; Grahn, Håkan; Hall, Johan

doi:10.1007/s00521-019-04163-3

Cited by 56 publications

(28 citation statements)

References 52 publications

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“…The first phase of methodology comprises the MSTAR image dataset collection and CNN is applied for the image classification on SAR images. The detailed architecture for the CNN model has been influenced from [16] and [17] with empirical modifications for best possible validation. The architecture diagram of proposed CNN is depicted in figure 3.…”

Section: Proposed Methodsmentioning

confidence: 99%

Deep learning-based explainable target classification for synthetic aperture radar images

Mandeep

Pannu

Malhi

2020

2020 13th International Conference on Human System Interaction (HSI)

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Deep learning has been extensively useful for its ability to mimic the human brain to make decisions. It is able to extract features automatically and train the model for classification and regression problems involved with complex images databases. This paper presents the image classification using Convolutional Neural Network (CNN) for target recognition using Synthetic-aperture Radar (SAR) database along with Explainable Artificial Intelligence (XAI) to justify the obtained results. In this work, we experimented with various CNN architectures on the MSTAR dataset, which is a special type of SAR images. Accuracy of target classification is almost 98.78% for the underlying preprocessed MSTAR database with given parameter options in CNN. XAI has been incorporated to explain the justification of test images by marking the decision boundary to reason the region of interest. Thus XAI based image classification is a robust prototype for automatic and transparent learning system while reducing the semantic gap between soft-computing and humans way of perception.

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Section: Proposed Methodsmentioning

confidence: 99%

Deep learning-based explainable target classification for synthetic aperture radar images

Mandeep

Pannu

Malhi

2020

2020 13th International Conference on Human System Interaction (HSI)

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“…In a similar method proposed by Romero et al [20], the heuristic line segmentation is further replaced with an HMM-based approach. Moving from HMM to convolutional approaches, Chammas et al [21] use convolutional neural network (CNN) features in combination with long short-term memory (LSTM) cells and connectionist temporal classification (CTC) to align text line images with their transcription. In their work, line segmentation is achieved heuristically by means of contour distribution analysis.…”

Section: Transcription Alignmentmentioning

confidence: 99%

Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

et al. 2021

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The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

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“…Moreover, in this project, the USPS test dataset of 2007 samples are also used for testing purposes. USPS [47,48] comprises 7291 training samples and 2007 testing samples in grayscale for the digits 0 to 9.…”

Section: Used Datasetsmentioning

confidence: 99%

An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

Ali

Pei

et al. 2020

Symmetry

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Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.

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ARDIS: a Swedish historical handwritten digit dataset

Cited by 56 publications

References 52 publications

Deep learning-based explainable target classification for synthetic aperture radar images

Deep learning-based explainable target classification for synthetic aperture radar images

Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

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