On-the-fly Historical Handwritten Text Annotation

The performance of information retrieval algorithms depends upon the availability of ground truth labels annotated by experts. This is an important prerequisite, and difficulties arise when the annotated ground truth labels are incorrect or incomplete due to high levels of degradation. To address this problem, this paper presents a simple method to perform on-the-fly annotation of degraded historical handwritten text in ancient manuscripts. The proposed method aims at quick generation of ground truth and correction of inaccurate annotations such that the bounding box perfectly encapsulates the word, and contains no added noise from the background or surroundings. This method will potentially be of help to historians and researchers in generating and correcting word labels in a document dynamically. The effectiveness of the annotation method is empirically evaluated on an archival manuscript collection from well-known publicly available datasets

Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing

Computation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document. This limits the applicability of quality metrics in applications such as hyperparameter optimization of image processing algorithms that operate on-the-fly on unseen documents. This work proposes the use of surrogate models to learn the behavior of a given document quality metric on existing datasets where ground truth images are available. The trained surrogate model can later be used to predict the metric value on previously unseen document images without requiring access to ground truth images. The surrogate model is empirically evaluated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets

A fuzzy approach for early human action detection / Ekta Vats

Early human action detection is an important computer vision task with a wide spectrum of potential applications. Most existing methods deal with the detection of an action after its completion. Contrarily, for early detection it is essential to detect an action as early as possible. Therefore, this thesis develops a solution to detect ongoing human action as soon as it begins, but before it finishes. In order to perform early human action detection, the conventional classification problem is modified into frame-by-frame level classification. There exists well-known classifiers such as Support Vector Machines (SVM), K-nearest Neighbour (KNN), etc. to perform action classification. However, the employability of these algorithms depends on the desired application and its requirements. Therefore, selection of the classifier to employ for the classification task is an important issue to be taken into account. The first part of the thesis studies this problem and fuzzy Bandler-Kohout (BK) sub-triangle product (subproduct) is employed as a classifier. The performance is tested for human action recognition and scene classification. This is a crucial step as it is the first attempt of using fuzzy BK subproduct for classification. The second part of this thesis studies the problem of early human action detection. The method proposed is based on fuzzy BK subproduct inference mechanism and utilizes the fuzzy capabilities in handling the uncertainties that exist in the real-world for reliable decision making. The fuzzy membership function generated frame-by-frame from fuzzy BK subproduct provides the basis to detect an action before it is completed, when a certain threshold is attained in a suitable way. In order to test the effectiveness of the proposed framework, a set of experiments is performed for few action sequences where the detector is able to recognize an action upon seeing �32% of the frames. iii Finally, the proposed method is analyzed from a broader perspective and a hybrid technique for early anticipation of human action is proposed. It combines the benefits of computer vision and fuzzy set theory based on fuzzy BK subproduct. The novelty lies in the construction of a frame-by-frame membership function for each kind of possible movement, taking into account several human actions from a publicly available dataset. Furthermore, the impact of various fuzzy implication operators and inference structures in retrieving the relationship between the human subject and the actions performed is discussed. The existing fuzzy implication operators are capable of handling only two dimensional data. A third dimension ‘time’ plays a crucial role in human action recognition to model the human movement changes over time. Therefore, a new space-time fuzzy implication operator is introduced, by modifying the existing implication operators to accommodate time as an added dimension. Empirically, the proposed hybrid technique is efficiently able to detect an action before completion and outperform the conventional solutions with good detection rate. The detector is able to identify an action upon viewing �23% of the frames on an average

Word Recognition using Embedded Prototype Subspace Classifiers on a New Imbalanced Dataset

This paper presents an approach towards word recognition based on embedded prototype subspace classification.The purpose of this paper is three-fold. Firstly, a new dataset for word recognition is presented, which is extractedfrom the Esposalles database consisting of the Barcelona cathedral marriage records. Secondly, different clusteringtechniques are evaluated for Embedded Prototype Subspace Classifiers. The dataset, containing 30 different classesof words is heavily imbalanced, and some word classes are very similar, which renders the classification task ratherchallenging. For ease of use, no stratified sampling is done in advance, and the impact of different data splits isevaluated for different clustering techniques. It will be demonstrated that the original clustering technique based onscaling the bandwidth has to be adjusted for this new dataset. Thirdly, an algorithm is therefore proposed that findskclusters, striving to obtain a certain amount of feature points in each cluster, rather than finding some clustersbased on scaling the Silverman’s rule of thumb. Furthermore, Self Organising Maps are also evaluated as both aclustering and embedding technique

Radial line Fourier descriptor for historical handwritten text representation

Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant properties of these descriptors amplify the noise in the degraded document images, rendering them more sensitive to noise and complex characteristics of historical manuscripts. Therefore, an efficient and relaxed feature descriptor is required as handwritten words across different documents are indeed similar, but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word representation, with a short feature vector of 32 dimensions. A segmentation-free and training-free handwritten word spotting method is studied herein that relies on the proposed RLF descriptor, takes into account different keypoint representations and uses a simple preconditioner-based feature matching algorithm. The effectiveness of the RLF descriptor for segmentation-free handwritten word spotting is empirically evaluated on well-known historical handwritten datasets using standard evaluation measures

Broad defects in the energy metabolism of leukocytes underlie immunoparalysis in sepsis

The acute phase of sepsis is characterized by a strong inflammatory reaction. At later stages in some patients, immunoparalysis may be encountered, which is associated with a poor outcome. By transcriptional and metabolic profiling of human patients with sepsis, we found that a shift from oxidative phosphorylation to aerobic glycolysis was an important component of initial activation of host defense. Blocking metabolic pathways with metformin diminished cytokine production and increased mortality in systemic fungal infection in mice. In contrast, in leukocytes rendered tolerant by exposure to lipopolysaccharide or after isolation from patients with sepsis and immunoparalysis, a generalized metabolic defect at the level of both glycolysis and oxidative metabolism was apparent, which was restored after recovery of the patients. Finally, the immunometabolic defects in humans were partially restored by therapy with recombinant interferon-γ, which suggested that metabolic processes might represent a therapeutic target in sepsis