An MLP using Hough transform based fuzzy feature extraction for Bengali script recognition
Identifieur interne : 000451 ( Istex/Corpus ); précédent : 000450; suivant : 000452An MLP using Hough transform based fuzzy feature extraction for Bengali script recognition
Auteurs : Shamik Sural ; P. K. DasSource :
- Pattern Recognition Letters [ 0167-8655 ] ; 1998.
Abstract
We define fuzzy sets on the Hough transform of character pattern pixels from which additional fuzzy sets are synthesized using t-norms. A multilayer perceptron trained with a number of linguistic set memberships derived from these t-norms can recognize characters of Bengali scripts by their similarities to different fuzzy pattern classes.
Url:
DOI: 10.1016/S0167-8655(99)00041-0
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<front><div type="abstract" xml:lang="en">We define fuzzy sets on the Hough transform of character pattern pixels from which additional fuzzy sets are synthesized using t-norms. A multilayer perceptron trained with a number of linguistic set memberships derived from these t-norms can recognize characters of Bengali scripts by their similarities to different fuzzy pattern classes.</div>
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<notesStmt><note type="content">Fig. 1: Different text zones in a line of scanned Bengli script.</note>
<note type="content">Fig. 2: Segmented image of a part of a scanned Bengali newspaper.</note>
<note type="content">Fig. 4: Characters of Bengali script recognized by different stages of MLP.</note>
<note type="content">Fig. 3: Recognition of a character with modifiers from a Bengali script.</note>
<note type="content">Fig. 5: (a) Two-state Markov chain model of a noisy document image. (b) Spatial propagation of noise burst in a document image.</note>
<note type="content">Fig. 6: (a) Variation of character recognition error in Bengali scripts with average burst length λ. (b) Variation of character recognition error in Bengali scripts with error density ratio Δ.</note>
<note type="content">Table 1: Membership functions of fuzzy sets defined on Hough transform accumulator cells. X and Y denote the height and the width of each character pattern</note>
<note type="content">Table 2: Some of the fuzzy features extracted by Hough transform from eight scanned Bengali characters</note>
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<abstract lang="en">We define fuzzy sets on the Hough transform of character pattern pixels from which additional fuzzy sets are synthesized using t-norms. A multilayer perceptron trained with a number of linguistic set memberships derived from these t-norms can recognize characters of Bengali scripts by their similarities to different fuzzy pattern classes.</abstract>
<note type="content">Fig. 1: Different text zones in a line of scanned Bengli script.</note>
<note type="content">Fig. 2: Segmented image of a part of a scanned Bengali newspaper.</note>
<note type="content">Fig. 4: Characters of Bengali script recognized by different stages of MLP.</note>
<note type="content">Fig. 3: Recognition of a character with modifiers from a Bengali script.</note>
<note type="content">Fig. 5: (a) Two-state Markov chain model of a noisy document image. (b) Spatial propagation of noise burst in a document image.</note>
<note type="content">Fig. 6: (a) Variation of character recognition error in Bengali scripts with average burst length λ. (b) Variation of character recognition error in Bengali scripts with error density ratio Δ.</note>
<note type="content">Table 1: Membership functions of fuzzy sets defined on Hough transform accumulator cells. X and Y denote the height and the width of each character pattern</note>
<note type="content">Table 2: Some of the fuzzy features extracted by Hough transform from eight scanned Bengali characters</note>
<note type="content">Table 3: Performance of the fuzzy OCR system in terms of speed and accuracy of recognition</note>
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