A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of Text Regions From Digital Engineering Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of Dimension Sets in Engineering Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector-Based Segmentation of Text Connected to Graphics in Engineering Drawings
SSPR '96 Proceedings of the 6th International Workshop on Advances in Structural and Syntactical Pattern Recognition
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
User term feedback in interactive text-based image retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic analysis and integration of architectural drawings
International Journal on Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new recognition model for electronic architectural drawings
Computer-Aided Design
Touching text character localization in graphical documents using SIFT
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A human-oriented image retrieval system using interactive genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Content retrieval of scanned line drawing images is a difficult problem, especially from real-life large scale databases. Existing algorithms don't work well due to their low efficiency by first recognizing various types of graphical primitives and then content-oriented texts. A new method for directly detecting texts from line drawing images is proposed in this paper. We first decompose a drawing image into a set of Local Consecutive Segments (LCSs). A LCS is defined as a minimum meaningful structural unit to imitate a stroke during human-drawing process. Next, we identify candidate character LCSs by statistical analysis and merge them into character LCS blocks by geometrical analysis. Finally, Hough transforms are applied to calculate the orientations of character LCS blocks and generate candidate strings. Experimental results show that our algorithm can well detect strings in any orientation. Our method is robust to text-graphic touching, scanning degradation and drawing noises, providing an efficient approach for content retrieval of document images.