NDLI: Statistical Grouping for Segmenting Symbols Parts from Line Drawings, with Application to Symbol Spotting

Content Provider	IEEE Xplore Digital Library
Author	Nayef, N. Breuel, T.M.
Copyright Year	2011
Abstract	In this work, we describe the use of statistical grouping for partitioning line drawings into shapes, those shapes represent meaningful parts of the symbols that constitute the line drawings. This grouping method converts a complete line drawing into a set of isolated shapes. This conversion has two effects: (1) making isolated recognition methods applicable for spotting symbols in context, (2) identifying potential regions of interest for symbol spotting methods, hence making them perform faster and more accurately. Our grouping is based on finding salient convex groups of geometric primitives, followed by combining certain found convex groups together. Additionally, we show how such grouping can be used for symbol spotting. When applied on a dataset of architectural line drawings the grouping method achieved above 98.8% recall and 97.3% precision for finding symbols parts. Using the grouping information, the spotting method achieved 99.3% recall and 99.9% precision. Compared to the performance of the same method without grouping information, an overall speed-up factor of 3.2 is achieved with the same -- or better -- recall and precision values.
Starting Page	364
Ending Page	368
File Size	664089
Page Count	5
File Format	PDF
ISBN	9781457713507
ISSN	15205363
e-ISBN	9780769545202
DOI	10.1109/ICDAR.2011.81
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Technical drawing Indexing Shape Context Jacobian matrices document analysis convex groups feature grouping symbol spotting
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Relational indexing of vectorial primitives for symbol spotting in line-drawing images.

Pattern Analysis and Applications DOI: 10.1007/s10044-009-0161-2 Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings

Intelligent image retrieval of visual cultural symbols

Employing Structural Representation for Symbol Detection, Symbol Spotting and Indexation in Line Drawing Document Images Abstract

Symbol spotting in vectorized technical drawings through a lookup table of region strings

Segmenting handwritten math symbols using adaboost and multi-scale shape context features.

Segmenting Handwritten Math Symbols Using AdaBoost and Multi-scale Shape Context Features

A Content Spotting System for Line Drawing Graphic Document Images

Spotting Symbols in Line Drawing Images Using Graph Representations

Statistical Grouping for Segmenting Symbols Parts from Line Drawings, with Application to Symbol Spotting

Similar Documents

Relational indexing of vectorial primitives for symbol spotting in line-drawing images.

Pattern Analysis and Applications DOI: 10.1007/s10044-009-0161-2 Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings

Intelligent image retrieval of visual cultural symbols

Employing Structural Representation for Symbol Detection, Symbol Spotting and Indexation in Line Drawing Document Images Abstract

Symbol spotting in vectorized technical drawings through a lookup table of region strings

Segmenting handwritten math symbols using adaboost and multi-scale shape context features.

Segmenting Handwritten Math Symbols Using AdaBoost and Multi-scale Shape Context Features

A Content Spotting System for Line Drawing Graphic Document Images

Spotting Symbols in Line Drawing Images Using Graph Representations

Statistical Grouping for Segmenting Symbols Parts from Line Drawings, with Application to Symbol Spotting