Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Digital consumer electronics handbook
An OCR System to Read Two Indian Language Scripts: Bangla and Devnagari (Hindi)
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A Complete OCR for Printed Hindi Text in Devanagari Script
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Challenges in OCR of Dev anagari Documents
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Machine Recognition of Online Handwritten Devanagari Characters
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Off-Line Handwritten Character Recognition of Devnagari Script
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Recognition of off-line handwritten devnagari characters using quadratic classifier
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Improved structuring element for handwriting and hand printed characters skeleton
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume I
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Devnagari script is a major script of India widely used for various languages. In this work, we propose a fuzzy stroke-based technique for analyzing handwritten Devnagari characters. After preprocessing, the character is segmented in strokes using our thinning and segmentation algorithm. We propose Average Compressed Direction Codes (ACDC) for shape description of segmented strokes. The strokes are classified as left curve, right curve, horizontal stroke, vertical stroke and slanted lines etc. We assign fuzzy weight to the strokes according to their circularity to find similarity between over segmented strokes and model strokes. The character is divided into nine zones and the occurrences of strokes in each zone and combinations of zones are found to contribute to Zonal Stroke Frequency (ZSF) and Regional Stroke Frequency (RSF) respectively. The classification space is partitioned on the basis of number of strokes, Zonal Stroke Frequency and Regional Stroke Frequency. The knowledge of script grammar is applied to classify characters using features like ACDC based stroke shape, relative strength, circularity and relative area. Euclidean distance classifier is applied for unordered stroke matching. The system tolerates slant of about 10° left and right and a skew of 5° up and down. The system proves to be fast and efficient with regard to space and time and gives high discrimination between similar characters and gives a recognition accuracy of 92.8%.