By Elisa Barney Smith, Xiaohui Qiu (auth.), Daniel Lopresti, Jianying Hu, Ramanujan Kashi (eds.)
This ebook constitutes the refereed court cases of the fifth foreign Workshop on record research platforms, DAS 2002, held in Princeton, NJ, united states in August 2002 with sponsorship from IAPR.
The forty four revised complete papers provided including 14 brief papers have been carefuly reviwed and chosen for inclusion within the e-book. All present matters in rfile research structures are adressed. The papers are geared up in topical sections on OCR beneficial properties and structures, handwriting attractiveness, format research, classifiers and studying, tables and kinds, textual content extraction, indexing and retrieval, rfile engineering, and new functions.
Read Online or Download Document Analysis Systems V: 5th International Workshop, DAS 2002 Princeton, NJ, USA, August 19–21, 2002 Proceedings PDF
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Additional resources for Document Analysis Systems V: 5th International Workshop, DAS 2002 Princeton, NJ, USA, August 19–21, 2002 Proceedings
98 better than the NN classiﬁer and BPN with the same set of features. However, the highest recognition rate of around 99 % is achieved with Haar wavelets. 6 %, respectively with a spread of 11. As before, the performance using Haar features is consistently better. Advantage of the RBF network over the BPN is that, training time is very less. 46 B. G. Ramakrishnan Table 10. 2 Recognition of Top and Right Matras The training set for top and right matras, contains 9 classes with 20 samples in each class and the test set contains 345 patterns.
IEEE Transaction on Speech and Audio Processing 7 (1999) 7. : Bilingual ocr for tamil and roman scripts. Master’s thesis, Department of Electrical Engineering, Indian Institute of Science (2001) 8. : A bootstrap technique for nearest neighbour classiﬁer design. IEEE Transaction on Pattern Analysis and Machine Intelligence 19 (1993) 73–79 Machine Recognition of Printed Kannada Text B. G. in Abstract. This paper presents the design of a full ﬂedged OCR system for printed Kannada text. The machine recognition of Kannada characters is diﬃcult due to similarity in the shapes of diﬀerent characters, script complexity and non-uniqueness in the representation of diacritics.
Basic alphabet set of Tamil script with corresponding diacritics Figure 1 shows the basic alphabet set of Tamil characters along with the modiﬁers or matras. One can see that some of the vowel modiﬁers appear as separate symbols and these are dealt as separate patterns for identiﬁcation. The * in the ﬁgure indicates that those matras change the entire shape of the characters diﬀerently for diﬀerent consonants. In such cases, each modiﬁed consonant is considered a separate class for identiﬁcation.