scispace - formally typeset
Search or ask a question
Author

Anukriti Bansal

Bio: Anukriti Bansal is an academic researcher from LNM Institute of Information Technology. The author has contributed to research in topics: Document layout analysis & Rain gauge. The author has an hindex of 3, co-authored 9 publications receiving 54 citations. Previous affiliations of Anukriti Bansal include Indian Institute of Technology, Jodhpur & Indian Institute of Technology Delhi.

Papers
More filters
Proceedings ArticleDOI
14 Dec 2014
TL;DR: A novel learning-based framework to identify tables from scanned document images as a structured labeling problem, which learns the layout of the document and labels its various entities as table header, table trailer, table cell and non-table region is presented.
Abstract: The paper presents a novel learning-based framework to identify tables from scanned document images. The approach is designed as a structured labeling problem, which learns the layout of the document and labels its various entities as table header, table trailer, table cell and non-table region. We develop features which encode the foreground block characteristics and the contextual information. These features are provided to a fixed point model which learns the inter-relationship between the blocks. The fixed point model attains a contraction mapping and provides a unique label to each block. We compare the results with Condition Random Fields(CRFs). Unlike CRFs, the fixed point model captures the context information in terms of the neighbourhood layout more efficiently. Experiments on the images picked from UW-III (University of Washington) dataset, UNLV dataset and our dataset consisting of document images with multicolumn page layout, show the applicability of our algorithm in layout analysis and table detection.

23 citations

Proceedings ArticleDOI
16 Dec 2012
TL;DR: This paper presents a new approach to detect tabular structures present in document images and in low resolution video images based on identifying the unique table start pattern and table trailer pattern and formulated perceptual attributes to characterize the patterns.
Abstract: This paper presents a new approach to detect tabular structures present in document images and in low resolution video images. The algorithm for table detection is based on identifying the unique table start pattern and table trailer pattern. We have formulated perceptual attributes to characterize the patterns. The performance of our table detection system is tested on a set of document images picked from UW-III (University of Washington) dataset, UNLV dataset, video images of NPTEL videos, and our own dataset. Our approach demonstrates improved detection for different types of table layouts, with or without ruling lines. We have obtained correct table localization on pages with multiple tables aligned side-by-side.

21 citations

Proceedings ArticleDOI
07 Apr 2014
TL;DR: A novel learning based framework to extract articles from newspaper images using a Fixed-Point Model that uses contextual information and features of each block to learn the layout of newspaper images and attains a contraction mapping to assign a unique label to every block.
Abstract: This paper presents a novel learning based framework to extract articles from newspaper images using a Fixed-Point Model. The input to the system comprises blocks of text and graphics, obtained using standard image processing techniques. The fixed point model uses contextual information and features of each block to learn the layout of newspaper images and attains a contraction mapping to assign a unique label to every block. We use a hierarchical model which works in two stages. In the first stage, a semantic label (heading, sub-heading, text-blocks, image and caption) is assigned to each segmented block. The labels are then used as input to the next stage to group the related blocks into news articles. Experimental results show the applicability of our algorithm in newspaper labeling and article extraction.

17 citations

Posted Content
TL;DR: Experimental analysis and comparison shows the applicability of the proposed deep and wide rainfall prediction model for rainfall prediction in Rajasthan with various deep-learning approaches like MLP, LSTM and CNN, which are observed to work well in sequence-based predictions.
Abstract: Rainfall is a natural process which is of utmost importance in various areas including water cycle, ground water recharging, disaster management and economic cycle. Accurate prediction of rainfall intensity is a challenging task and its exact prediction helps in every aspect. In this paper, we propose a deep and wide rainfall prediction model (DWRPM) and evaluate its effectiveness to predict rainfall in Indian state of Rajasthan using historical time-series data. For wide network, instead of using rainfall intensity values directly, we are using features obtained after applying a convolutional layer. For deep part, a multi-layer perceptron (MLP) is used. Information of geographical parameters (latitude and longitude) are included in a unique way. It gives the model a generalization ability, which helps a single model to make rainfall predictions in different geographical conditions. We compare our results with various deep-learning approaches like MLP, LSTM and CNN, which are observed to work well in sequence-based predictions. Experimental analysis and comparison shows the applicability of our proposed method for rainfall prediction in Rajasthan.

4 citations

Proceedings ArticleDOI
24 Aug 2013
TL;DR: A novel framework for learning optimal parameters for text graphic separation in the presence of complex layouts of Indian newspaper is proposed.
Abstract: Digitization of newspaper article is important for registering historical events. Layout analysis of Indian newspaper is a challenging task due to the presence of different font size, font styles and random placement of text and non-text regions. In this paper we propose a novel framework for learning optimal parameters for text graphic separation in the presence of complex layouts. The learning problem has been formulated as an optimization problem using EM algorithm to learn optimal parameters depending on the nature of the document content.

3 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Books and internet are the recommended media to help you improving your quality and performance.
Abstract: Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.

326 citations

Proceedings ArticleDOI
01 Nov 2017
TL;DR: The proposed method works with high precision on document images with varying layouts that include documents, research papers, and magazines and beats Tesseract's state of the art table detection system by a significant margin.
Abstract: Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to the reader in a structured manner. It is a hard problem due to varying layouts and encodings of the tables. Researchers have proposed numerous techniques for table detection based on layout analysis of documents. Most of these techniques fail to generalize because they rely on hand engineered features which are not robust to layout variations. In this paper, we have presented a deep learning based method for table detection. In the proposed method, document images are first pre-processed. These images are then fed to a Region Proposal Network followed by a fully connected neural network for table detection. The proposed method works with high precision on document images with varying layouts that include documents, research papers, and magazines. We have done our evaluations on publicly available UNLV dataset where it beats Tesseract's state of the art table detection system by a significant margin.

159 citations

01 Jan 2007
TL;DR: The journal not only will address research topics related to networking and communications theory, but will also consider the standardization, economic, and engineering aspects of P2P technologies, and their impacts on software engineering, computer engineering, networked communication, and security.
Abstract: Aims & Scope Peer-to-Peer Networking and Applications (P2PNA) has received significant attention from both academia and industry in recent years. The aim of the Peer-to-Peer Networking and Applications journal is to disseminate state-of-the-art research and development results in this rapidly growing research area, to facilitate the deployment of P2P networking and applications, and to bring together the academic and industry communities, with the goal of fostering interaction to promote further research interests and activities, thus enabling new P2P applications and services. The journal not only will address research topics related to networking and communications theory, but will also consider the standardization, economic, and engineering aspects of P2P technologies, and their impacts on software engineering, computer engineering, networked communication, and security.

76 citations

Journal ArticleDOI
TL;DR: Experiments on the ICDAR 2013 dataset show that the results obtained are very encouraging and proves the effectiveness and superiority of the proposed method.
Abstract: Table detection is a challenging problem and plays an important role in document layout analysis. In this paper, we propose an effective method to identify the table region from document images. First, the regions of interest (ROIs) are recognized as the table candidates. In each ROI, we locate text components and extract text blocks. After that, we check all text blocks to determine if they are arranged horizontally or vertically and compare the height of each text block with the average height. If the text blocks satisfy a series of rules, the ROI is regarded as a table. Experiments on the ICDAR 2013 dataset show that the results obtained are very encouraging. This proves the effectiveness and superiority of our proposed method.

55 citations

Book ChapterDOI
23 Aug 2020
TL;DR: In this paper, the authors focus on tables that have complex structures, dense content, and varying layouts with no dependency on meta-features and/or optical character recognition (OCR) models.
Abstract: Tables are information-rich structured objects in document images. While significant work has been done in localizing tables as graphic objects in document images, only limited attempts exist on table structure recognition. Most existing literature on structure recognition depends on extraction of meta-features from the pdf document or on the optical character recognition (ocr) models to extract low-level layout features from the image. However, these methods fail to generalize well because of the absence of meta-features or errors made by the ocr when there is a significant variance in table layouts and text organization. In our work, we focus on tables that have complex structures, dense content, and varying layouts with no dependency on meta-features and/or ocr.

53 citations