1 / 10

Statistical and Structural Approaches for Technical Symbol Recognition Using XML Modelling

This research presents a novel system for technical symbol recognition leveraging both statistical and structural methodologies. Implementing XML modeling, the system effectively captures recognition results through various treatment steps, including loop extraction, blob coloring and filtering, feature extraction, and statistical classification. The results are promising—achieving 99% loop recognition accuracy and demonstrating robust performance even with noise, yielding an overall symbol recognition rate of 86.86%. Future perspectives include exploiting sub-graph isomorphism and structural models for enhanced recognition capabilities.

jed
Download Presentation

Statistical and Structural Approaches for Technical Symbol Recognition Using XML Modelling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A statistical and structural approach for symbol recognition, using XML modelling Mathieu Delalandre, Pierre Héroux, Sébastien Adam, Eric Trupin, Jean-Marc Ogier UFR Sciences et techniques université de Rouen 76130 Mont Saint Aignan

  2. Introduction • Description : System using a statistical and structural approach for technical symbol recognitionwithXML modelling of recognition results Utility map extract XML modelling of recognition results

  3. Treatment steps Loop extraction (1) Blob coloring filtering (2) Feature extraction (3) Statistical classification (4) XML modelling of recognition results Image processing Model reconstruction (5) Recognition processing Structural classification (6) XML Modelling

  4. (1) Loop extraction

  5. (2) Blob coloring filtering

  6. (3)&(4) Feature extraction & Statistical classification (4)Feature extraction *Fourrier-Mellin invariants *Zernike moments *Circular probes (5)Statistical classifier kppv

  7. (5)&(6) Model reconstruction& Structural classification (5)Model reconstruction Connection and/or distance contraints (6)Structural classifcation Similarity criterion based on common sub-graph

  8. Results • Statistical results • 50 loops for training and test set • 99% of loop recognition • Structural results • 9 plan extracts, ~100 loops, ~30 symbols • Without noise on statistical classification • 100% of loop recognition gives 100% of symbol recognition • With noise on statistical classification • 55.4% of loop recognition gives 86.86% of symbol recognition

  9. Perspectives (1) • Exploiting sub-graph isomorphism

  10. Perspectives (2) • Exploiting structural models

More Related