Other affiliations: University of Mysore
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.
Papers published on a yearly basis
TL;DR: A review of the OCR work done on Indian language scripts and the scope of future work and further steps needed for Indian script OCR development is presented.
TL;DR: A complete Optical Character Recognition (OCR) system for printed Bangla, the fourth most popular script in the world, is presented and extension of the work to Devnagari, the third most popular Script in the World, is discussed.
01 Nov 2017
TL;DR: This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge, which aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together.
Abstract: Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.
25 Jun 2020
TL;DR: A deep learning-based Convolutional Neural Network model, which is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases, and proves the viability of using the proposed Truncated Inception Net as a screening tool.
Abstract: Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in a short period of time, and the infection, caused by SARS-CoV-2, is spreading rapidly AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs) In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account The proposed model achieved an accuracy of 9996% (AUC of 10) in classifying COVID-19 positive cases from combined Pneumonia and healthy cases Similarly, it achieved an accuracy of 9992% (AUC of 099) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis, and healthy CXRs To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using the acquired CXRs, and proves the viability of using the proposed Truncated Inception Net as a screening tool
••18 Aug 1997
TL;DR: An OCR system is proposed that can read two Indian language scripts: Bangla and Devnagari (Hindi), the most popular ones in the Indian subcontinent, and shows a good performance for single font scripts printed on clear documents.
Abstract: An OCR system is proposed that can read two Indian language scripts: Bangla and Devnagari (Hindi), the most popular ones in the Indian subcontinent. These scripts, having the same origin in ancient Brahmi script, have many features in common and hence a single system can be modeled to recognize them. In the proposed model, document digitization, skew detection, text line segmentation and zone separation, word and character segmentation, character grouping into basic, modifier and compound character category are done for both scripts by the same set of algorithms. The feature sets and classification tree as well as the knowledge base required for error correction (such as lexicon) differ for Bangla and Devnagari. The system shows a good performance for single font scripts printed on clear documents.
01 Jan 2015
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.
01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.
15 Oct 2004