Umapada Pal

Bio: Umapada Pal is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Feature extraction & Handwriting recognition. The author has an hindex of 47, co-authored 478 publications receiving 9925 citations. Previous affiliations of Umapada Pal include University of Mysore.

LOOP Descriptor: Local Optimal-Oriented Pattern

Abstract: This letter introduces the LOOP binary descriptor (local optimal-oriented pattern) that encodes rotation invariance into the main formulation itself. This makes any post processing stage for rotation invariance redundant and improves on both accuracy and time complexity. We consider fine-grained lepidoptera (moth/butterfly) species recognition as the representative problem since it involves repetition of localized patterns and textures that may be exploited for discrimination. We evaluate the performance of LOOP against its predecessors as well as few other popular descriptors. Besides experiments on standard benchmarks, we also introduce a new small image dataset on NZ Lepidoptera. LOOP performs as well or better on all datasets evaluated compared to previous binary descriptors. The new dataset and demo code of the proposed method are available through the lead author's academic webpage and GitHub.

ICDAR 2013 Competitions on Signature Verification and Writer Identification for On- and Offline Skilled Forgeries (SigWiComp 2013)

Abstract: This paper presents the results of the ICDAR2013 competitions on signature verification and writer identification for on- and offline skilled forgeries jointly organized by PR researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. Two modalities (signatures, and handwritten text) are considered where training and evaluation data (in Dutch and Japanese) were collected and provided by FHEs and PR-researchers. Four tasks were defined where the systems had to perform Dutch offline signature verification, Japanese offline signature verification, Japanese online signature verification, and Dutch writer identification. The participants of the signatures modality were motivated to report their results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic casework. For evaluation of signatures modality, we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Cllr). The system having the smallest value of the Minimum Cost of Log Likelihood Ratio (Cllrmin) is declared winner. For evaluation of the handwritten text modality, we used the precision and accuracy measures and winners are announced on the basis of best F-measure value.

Effects of Degradations on Deep Neural Network Architectures.

Abstract: Recently, image classification methods based on capsules (groups of neurons) and a novel dynamic routing protocol are proposed. The methods show promising performances than the state-of-the-art CNN-based models in some of the existing datasets. However, the behavior of capsule-based models and CNN-based models are largely unknown in presence of noise. So it is important to study the performance of these models under various noises. In this paper, we demonstrate the effect of image degradations on deep neural network architectures for image classification task. We select six widely used CNN architectures to analyse their performances for image classification task on datasets of various distortions. Our work has three main contributions: 1) we observe the effects of degradations on different CNN models; 2) accordingly, we propose a network setup that can enhance the robustness of any CNN architecture for certain degradations, and 3) we propose a new capsule network that achieves high recognition accuracy. To the best of our knowledge, this is the first study on the performance of CapsuleNet (CapsNet) and other state-of-the-art CNN architectures under different types of image degradations. Also, our datasets and source code are available publicly to the researchers.

Multioriented and curved text lines extraction from Indian documents

Abstract: There are printed artistic documents where text lines of a single page may not be parallel to each other. These text lines may have different orientations or the text lines may be curved shapes. For the optical character recognition (OCR) of these documents, we need to extract such lines properly. In this paper, we propose a novel scheme, mainly based on the concept of water reservoir analogy, to extract individual text lines from printed Indian documents containing multioriented and/or curve text lines. A reservoir is a metaphor to illustrate the cavity region of a character where water can be stored. In the proposed scheme, at first, connected components are labeled and identified either as isolated or touching. Next, each touching component is classified either straight type (S-type) or curve type (C-type), depending on the reservoir base-area and envelope points of the component. Based on the type (S-type or C-type) of a component two candidate points are computed from each touching component. Finally, candidate regions (neighborhoods of the candidate points) of the candidate points of each component are detected and after analyzing these candidate regions, components are grouped to get individual text lines.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.