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Handwritten Character Recognition using Elastic Matching and PCA

Handwritten Character Recognition using Elastic Matching and PCA. Vanita Mane, Lena Ragha International Conference on Advances in Conputing , Communication and Control. Reporter: 資訊所 P78991121 Yung-Chih Cheng ( 鄭詠之 ). Outline. Introduction Data Collection System Architecture

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Handwritten Character Recognition using Elastic Matching and PCA

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  1. Handwritten Character Recognition using Elastic Matching and PCA Vanita Mane, Lena Ragha International Conference on Advances in Conputing, Communication and Control Reporter: 資訊所 P78991121 Yung-Chih Cheng (鄭詠之)

  2. Outline • Introduction • Data Collection • System Architecture • Feature Extraction • Recognition Methods • Results and Discussion • Conclusion

  3. Introduction (1/2) • Character recognition is becoming more and more important in the modern world. • Handwritten recognition is not a new technology • Optical Character Recognition(OCR) • Automatic reading of optically sensor • Document text materials to translate human-readable character to machine readable codes

  4. Introduction (2/2) • Less work has been reported for the recognition of Indian Languages. • Complexity of the shape • Large set of different patterns • Propose a new elastic image matching technique based on an eigen-deformation • Offline isolated English uppercase handwritten characters • Offline isolated handwritten character of Devnagari

  5. Data Collection-English • ETL6 standard database is used • Japanese Characters • English Capital letters [A-Z] • Digits [0-9] • .pgn file format • Image size: 64 x 63 • Collect 500 data samples from different individuals of various professions for the experimetn.

  6. Data Collection-Devnagari • Devnagari is the most popular script in Indian and the most popular Indian language Hindi is written an Devnagari script • National language of India • The third most popular language in the world • 14 vowels and 33 consonants • Left to right • In this paper, considering 12 basic character

  7. System Architecture (1/2)

  8. System Architecture (2/2) • Preprocessing • Filtering • Morphological operation • Normalization • Segmentation • Horizontal Segmentation • Vertical Segmentation • Size Normalization

  9. Feature Extraction (1/2) • Elastic Matching

  10. Feature Extraction (2/2) • Properties of Elastic Matching(EM) • Anisotropic • Asymmetric • The distance between image T and R • R is deformed by F for optimal matching T and R • Symmetric • Sum of two asymmetric distances is simplest and gives result than individual asymmetric elastic matching tecnique

  11. Recognition Methods (1/2) • With Eigen-deformations(PCA) • Training Phase • Recognition Phase

  12. Recognition Methods (2/2) • Without Eigen-deformation

  13. Results and Discussion (1/2)

  14. Results and Discussion (2/2)

  15. Conclusion (1/2) • A elastic matching with PCA based system towards the recognition of off-line isolated uppercase English character and Devnagari handwritten character • Depend on the characteristics of the elastic matching emplyed to collect actual deformations

  16. Conclusion (2/2) • The distribution of the actual deformation is not isotropic and therefore can be approcimated by several principal axes.

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