Optimization of the Anisotropic Gaussian Kernel for Text Segmentation and Parameter Extraction

Brodić, Darko

doi:10.1007/978-3-642-15240-5_11

Cited by 8 publications

(8 citation statements)

References 7 publications

(17 reference statements)

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“…This leads to K ≈ 15% of letter height [25] and L ≈ 5 x K. However, the results from all test experiments are interrelated. Hence, they can be combined after some decisionmaking.…”

Section: Results Comparative Analysis and Decision-makingmentioning

confidence: 96%

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Methodology for the Evaluation of the Algorithms for Text Line Segmentation Based on Extended Binary Classification

Brodić¹

2011

Measurement Science Review

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“…This leads to K ≈ 15% of letter height [25] and L ≈ 5 x K. However, the results from all test experiments are interrelated. Hence, they can be combined after some decisionmaking.…”

Section: Results Comparative Analysis and Decision-makingmentioning

confidence: 96%

“…According to that, K should be chosen from 10% to 20% of the letter height [25]. Obtained results for different text samples are presented in Tables 2-4.…”

Section: Results Comparative Analysis and Decision-makingmentioning

confidence: 99%

Methodology for the Evaluation of the Algorithms for Text Line Segmentation Based on Extended Binary Classification

Brodić¹

2011

Measurement Science Review

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“…It is defined as λ = K / L . λ is selected from the reduced set {3, 4, 5} [ 21 , 22 ]. All text samples are converted to 300 dpi resolution.…”

Section: Testing and Evaluationmentioning

confidence: 99%

“…Regarding the above results, it is clear that from the testing values of the parameter pair ( K and λ ), the best response of the algorithm to the various types of text is obtained for the pair (10, 4) [ 22 ]. The RMSE evaluation confirms it.…”

Section: Testing and Evaluationmentioning

confidence: 99%

An Approach to a Comprehensive Test Framework for Analysis and Evaluation of Text Line Segmentation Algorithms

Brodić

Milivojević

Milivojević³

2011

Sensors

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The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures.

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“…Some theoretical extensions are given in the form of Gabor transformation [6]. Further, original method is intensively investigated in [7].…”

Section: Introductionmentioning

confidence: 99%

Approach to the Improvement of the Text Line Segmentation by Oriented Anisotropic Gaussian Kernel

Brodić

Milivojević

Milivojević³

2012

ElAEE

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The paper presents the approach to the algorithm for text line segmentation based on the anisotropic Gaussian kernel. As a result of this algorithm the growing region around text is exploited. Furthermore, anisotropic Gaussian kernel is rotated to improve text line segmentation process. Text objects orientation is evaluated by binary moments. For test purposes algorithm is evaluated under different text samples. From the obtained results comparative analysis between algorithm with anisotropic and oriented Gaussian kernel is made. At the end, benefits of the extended approach are revealed. Ill. 7, bibl. 22, tabl. 5 (in English; abstracts in English and Lithuanian).

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Optimization of the Anisotropic Gaussian Kernel for Text Segmentation and Parameter Extraction

Cited by 8 publications

References 7 publications

Methodology for the Evaluation of the Algorithms for Text Line Segmentation Based on Extended Binary Classification

Methodology for the Evaluation of the Algorithms for Text Line Segmentation Based on Extended Binary Classification

An Approach to a Comprehensive Test Framework for Analysis and Evaluation of Text Line Segmentation Algorithms

Approach to the Improvement of the Text Line Segmentation by Oriented Anisotropic Gaussian Kernel

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