Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data. Drawing intuition from the success achieved by Convolutional Neural Networks (CNNs) by going deeper, we introduce DeepCaps, a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. With DeepCaps, we surpass the state-of-the-art capsule domain networks results on CIFAR10, SVHN and Fashion MNIST, while achieving a 68% reduction in the number of parameters. Further, we propose a class independent decoder network, which strengthens the use of reconstruction loss as a regularization term. This leads to an interesting property of the decoder, which allows us to identify and control the physical attributes of the images represented by the instantiation parameters.

/pdf/deepcaps-going-deeper-with-capsule-networks-4czujoa5hk.pdf

DeepCaps: Going Deeper With Capsule Networks

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Capsule Networks – A survey

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.

Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models

Over the past few years, deep neural networks (DNNs) have garnered remarkable success in a diverse range of real-world applications. However, DNNs consider a large number of inputs and consist of a large number of parameters, resulting in high computational demand. We study the human somatosensory system and propose the SpinalNet to achieve higher accuracy with less computational resources. In a typical neural network (NN) architecture, the hidden layers receive inputs in the first layer and then transfer the intermediate outcomes to the next layer. In the proposed SpinalNet, the structure of hidden layers allocates to three sectors: 1) Input row, 2) Intermediate row, and 3) output row. The intermediate row of the SpinalNet contains a few neurons. The role of input segmentation is in enabling each hidden layer to receive a part of the inputs and outputs of the previous layer. Therefore, the number of incoming weights in a hidden layer is significantly lower than traditional DNNs. As all layers of the SpinalNet directly contributes to the output row, the vanishing gradient problem does not exist. We also investigate the SpinalNet fully-connected layer to several well-known DNN models and perform traditional learning and transfer learning. We observe significant error reductions with lower computational costs in most of the DNNs. We have also obtained the state-of-the-art (SOTA) performance for QMNIST, Kuzushiji-MNIST, EMNIST (Letters, Digits, and Balanced), STL-10, Bird225, Fruits 360, and Caltech-101 datasets. The scripts of the proposed SpinalNet are available with the following link: this https URL

SpinalNet: Deep Neural Network with Gradual Input.

Many localized languages struggle to reap the benefits of recent advancements in character recognition systems due to the lack of substantial amount of labeled training data. This is due to the difficulty in generating large amounts of labeled data for such languages and inability of deep learning techniques to properly learn from small number of training samples. We solve this problem by introducing a technique of generating new training samples from the existing samples, with realistic augmentations which reflect actual variations that are present in human hand writing, by adding random controlled noise to their corresponding instantiation parameters. Our results with a mere 200 training samples per class surpass existing character recognition results in the EMNIST-letter dataset while achieving the existing results in the three datasets: EMNIST-balanced, EMNIST-digits, and MNIST. We also develop a strategy to effectively use a combination of loss functions to improve reconstructions. Our system is useful in character recognition for localized languages that lack much labeled training data and even in other related more general contexts such as object recognition.

TextCaps: Handwritten Character Recognition With Very Small Datasets

TextCaps : Handwritten Character Recognition with Very Small Datasets

Abstract With the improvement of electronic circuit production methods, such as reduction of component size and the increase of component density, the risk of defects is increasing in the production line. Many techniques have been incorporated to check for failed solder joints, such as X-ray imaging, optical imaging and thermal imaging, among which X-ray imaging can inspect external and internal defects. However, some advanced algorithms are not accurate enough to meet the requirements of quality control. A lot of manual inspection is required that increases the specialist workload. In addition, automatic X-ray inspection could produce incorrect region of interests that deteriorates the defect detection. The high-dimensionality of X-ray images and changes in image size also pose challenges to detection algorithms. Recently, the latest advances in deep learning provide inspiration for image-based tasks and are competitive with human level. In this work, deep learning is introduced in the inspection for quality control. Four joint defect detection models based on artificial intelligence are proposed and compared. The noisy ROI and the change of image dimension problems are addressed. The effectiveness of the proposed models is verified by experiments on real-world 3D X-ray dataset, which saves the specialist inspection workload greatly. 

/pdf/deep-learning-based-solder-joint-defect-detection-on-19cx5ret.pdf

Deep learning based solder joint defect detection on industrial printed circuit board X-ray images

Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data. Drawing intuition from the success achieved by Convolutional Neural Networks (CNNs) by going deeper, we introduce DeepCaps1, a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. With DeepCaps, we surpass the state-of-the-art results in the capsule network domain on CIFAR10, SVHN and Fashion MNIST, while achieving a 68% reduction in the number of parameters. Further, we propose a class-independent decoder network, which strengthens the use of reconstruction loss as a regularization term. This leads to an interesting property of the decoder, which allows us to identify and control the physical attributes of the images represented by the instantiation parameters.

Hirunima Jayasekara

Papers

DeepCaps: Going Deeper With Capsule Networks

TextCaps: Handwritten Character Recognition With Very Small Datasets

TextCaps : Handwritten Character Recognition with Very Small Datasets

Deep learning based solder joint defect detection on industrial printed circuit board X-ray images

DeepCaps: Going Deeper with Capsule Networks