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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Handwriting Moroccan regions recognition using Tifinagh character</title><author><name sortKey="El Kessab, B" sort="El Kessab, B" uniqKey="El Kessab B" first="B." last="El Kessab">B. El Kessab</name></author><author><name sortKey="Daoui, C" sort="Daoui, C" uniqKey="Daoui C" first="C." last="Daoui">C. Daoui</name></author><author><name sortKey="Bouikhalene, B" sort="Bouikhalene, B" uniqKey="Bouikhalene B" first="B." last="Bouikhalene">B. Bouikhalene</name></author><author><name sortKey="Salouan, R" sort="Salouan, R" uniqKey="Salouan R" first="R." last="Salouan">R. Salouan</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26966718</idno><idno type="pmc">4783523</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4783523</idno><idno type="RBID">PMC:4783523</idno><idno type="doi">10.1016/j.dib.2015.07.018</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000129</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000129</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Handwriting Moroccan regions recognition using Tifinagh character</title><author><name sortKey="El Kessab, B" sort="El Kessab, B" uniqKey="El Kessab B" first="B." last="El Kessab">B. El Kessab</name></author><author><name sortKey="Daoui, C" sort="Daoui, C" uniqKey="Daoui C" first="C." last="Daoui">C. Daoui</name></author><author><name sortKey="Bouikhalene, B" sort="Bouikhalene, B" uniqKey="Bouikhalene B" first="B." last="Bouikhalene">B. Bouikhalene</name></author><author><name sortKey="Salouan, R" sort="Salouan, R" uniqKey="Salouan R" first="R." last="Salouan">R. Salouan</name></author></analytic><series><title level="j">Data in Brief</title><idno type="eISSN">2352-3409</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The territorial organization of Morocco during administratives division of 2009 is based on 16 regions. In this work we will create a system of recognition of handwritten words (names of regions) using the Amazigh language is an official language by the Moroccan Royal Institute of Amazigh Culture (IRCAM) (2003a) <xref rid="bib1" ref-type="bibr">[1]</xref> such as this language is slightly treated by researchers in pattern recognition field that is why we decided to study this language (El Kessab et al., 2013 <xref rid="bib3" ref-type="bibr">[3]</xref>; El Kessab et al., 2014 <xref rid="bib4" ref-type="bibr">[4]</xref>) that knowing the state make a decision to computerize the various public sectors by this language.</p><p>In this context we propose a data set for handwritten Tifinagh regions composed of 1600 image (100 Image for each region). The dataset can be used in one hand to test the efficiency of the Tifinagh region recognition system in extraction of characteristics significatives and the correct identification of each region in classification phase in the other hand.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="El Kessab, B" uniqKey="El Kessab B">B. El Kessab</name></author><author><name sortKey="Daoui, C" uniqKey="Daoui C">C. Daoui</name></author><author><name sortKey="Bouikhalene, B" uniqKey="Bouikhalene B">B. Bouikhalene</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="El Kessab, B" uniqKey="El Kessab B">B. El Kessab</name></author><author><name sortKey="Daoui, C" uniqKey="Daoui C">C. Daoui</name></author><author><name sortKey="Bouikhalene, B" uniqKey="Bouikhalene B">B. Bouikhalene</name></author><author><name sortKey="Salouan, R" uniqKey="Salouan R">R. Salouan.</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sadiqi, Fatima" uniqKey="Sadiqi F">Fatima Sadiqi</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><pmc article-type="brief-report"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Data Brief</journal-id><journal-id journal-id-type="iso-abbrev">Data Brief</journal-id><journal-title-group><journal-title>Data in Brief</journal-title></journal-title-group><issn pub-type="epub">2352-3409</issn><publisher><publisher-name>Elsevier</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">26966718</article-id><article-id pub-id-type="pmc">4783523</article-id><article-id pub-id-type="publisher-id">S2352-3409(15)00137-7</article-id><article-id pub-id-type="doi">10.1016/j.dib.2015.07.018</article-id><article-categories><subj-group subj-group-type="heading"><subject>Data Article</subject></subj-group></article-categories><title-group><article-title>Handwriting Moroccan regions recognition using Tifinagh character</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>El Kessab</surname><given-names>B.</given-names></name><email>bade10@hotmail.fr</email></contrib><contrib contrib-type="author"><name><surname>Daoui</surname><given-names>C.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Bouikhalene</surname><given-names>B.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Salouan</surname><given-names>R.</given-names></name></contrib></contrib-group><aff id="aff0005">Laboratory of Information Processing and Decision Support, Morocco</aff><pub-date pub-type="pmc-release"><day>26</day><month>7</month><year>2015</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="collection"><month>9</month><year>2015</year></pub-date><pub-date pub-type="epub"><day>26</day><month>7</month><year>2015</year></pub-date><volume>4</volume><fpage>534</fpage><lpage>543</lpage><history><date date-type="received"><day>20</day><month>6</month><year>2015</year></date><date date-type="rev-recd"><day>10</day><month>7</month><year>2015</year></date><date date-type="accepted"><day>13</day><month>7</month><year>2015</year></date></history><permissions><copyright-statement>© 2015 The Authors</copyright-statement><copyright-year>2015</copyright-year><license license-type="CC BY" xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</license-p></license></permissions><abstract><p>The territorial organization of Morocco during administratives division of 2009 is based on 16 regions. In this work we will create a system of recognition of handwritten words (names of regions) using the Amazigh language is an official language by the Moroccan Royal Institute of Amazigh Culture (IRCAM) (2003a) <xref rid="bib1" ref-type="bibr">[1]</xref> such as this language is slightly treated by researchers in pattern recognition field that is why we decided to study this language (El Kessab et al., 2013 <xref rid="bib3" ref-type="bibr">[3]</xref>; El Kessab et al., 2014 <xref rid="bib4" ref-type="bibr">[4]</xref>) that knowing the state make a decision to computerize the various public sectors by this language.</p><p>In this context we propose a data set for handwritten Tifinagh regions composed of 1600 image (100 Image for each region). The dataset can be used in one hand to test the efficiency of the Tifinagh region recognition system in extraction of characteristics significatives and the correct identification of each region in classification phase in the other hand.</p></abstract></article-meta></front><body><p>Specifications table<table-wrap id="t0010" position="float"><table frame="hsides" rules="groups"><tbody><tr><td>Subject area</td><td>Computer science</td></tr><tr><td>More specific subject area</td><td>Image processing, handwritten Tifinagh region, the Amazigh language</td></tr><tr><td>Type of data</td><td>Image</td></tr><tr><td>How data was acquired</td><td>Handwritten, Scanner, Marker</td></tr><tr><td>Data format</td><td>Jpeg image</td></tr><tr><td>Experimental factors</td><td>We ask 70 students to write 16 regions with Tifinagh characters, we use an HP G3110 with maximum resolution 4800×9600 dpi to data scan, and we use a marker in writing of characters</td></tr><tr><td>Experimental features</td><td>1376 Image with a size of 30×30 pixels (100 images/region)</td></tr><tr><td>Data source location</td><td>Béni Mellal, Morocco</td></tr><tr><td>Data accessibility</td><td>Within this article</td></tr></tbody></table></table-wrap></p><p>Value of the data<list list-type="simple"><list-item id="u0005"><label>•</label><p>The region is the current highest administrative division of Morocco. The regions are subdivided into a total of 63s-order administrative divisions, which are prefectures and provinces <xref rid="bib2" ref-type="bibr">[2]</xref> A Moroccan region is governed by a Wali, nominated by the King. The Wali is also governor of the province (or prefecture) where he resides.</p></list-item><list-item id="u0010"><label>•</label><p>As part of a 1997 decentralization and regionalization law passed by the legislature 16 new regions of Morocco were created.</p></list-item><list-item id="u0015"><label>•</label><p>We chose a database word contains 1000 words written in marker and that represents the 16 region of Morocco.</p></list-item><list-item id="u0020"><label>•</label><p>Optical Character Recognition (OCR) can be applied on both cases printed or handwritten. In this work we use several efficient techniques in each of the three principal phases forming a the system of recognition which are firstly the pre-processing then secondly the features extraction then finally learning-classification several studies has been done for recognition of Handwritten Tifinagh regions recognition by using in the features extraction phase the square and triangular zoning method in one hand or in the learning-classification phase the support vectors machines (SVM) and the neural networks on the other hand.</p></list-item><list-item id="u0025"><label>•</label><p>Amazigh alphabet is considered as a national language since a new constitution of 2011 is a creative field <xref rid="bib3" ref-type="bibr">[3]</xref>, <xref rid="bib4" ref-type="bibr">[4]</xref>, <xref rid="bib5" ref-type="bibr">[5]</xref>, <xref rid="bib6" ref-type="bibr">[6]</xref>, <xref rid="bib7" ref-type="bibr">[7]</xref> is very useful to create a system for Tifinagh hand writing words representing the regions.</p></list-item></list></p><sec id="s0005"><title>Experimental design, materials and methods</title><p>For several years, on-line and off-line handwriting character recognition has been considered as a very dynamic field given that its applicability in many different domains such as bank check processing, automatic data entry and postal sorting, The postal automation, bank checks identification, automatic processing of administrative files, etc. In this work we have presented the steps of the recognition system in <xref rid="f0005" ref-type="fig">Fig. 1</xref>.</p><p>We chose a database word contains 1000 words written with marker and that represents sixteen region of Morocco <xref rid="t0005" ref-type="table">Table 1</xref>.</p><p>The extraction steps were<list list-type="simple"><list-item id="u0030"><label>•</label><p>We ask 70 students (in Laboratory of Information Processing and Decision Support) to write the 16 region with Tifinagh characters (<xref rid="f0015" ref-type="fig">Fig. 3</xref>).</p></list-item><list-item id="u0035"><label>•</label><p>The direction of writing of this character is the left to right in horizontal lines.</p></list-item><list-item id="u0040"><label>•</label><p>The characters are written in a way separated in the text (see <xref rid="f0010" ref-type="fig">Fig. 2</xref>, <xref rid="f0015" ref-type="fig">Fig. 3</xref>).</p></list-item><list-item id="u0045"><label>•</label><p>Each original region image has a size equal to 30×30 pixels (<xref rid="f0020" ref-type="fig">Fig. 4</xref>, <xref rid="f0025" ref-type="fig">Fig. 5</xref>, <xref rid="f0030" ref-type="fig">Fig. 6</xref>, <xref rid="f0035" ref-type="fig">Fig. 7</xref>, <xref rid="f0040" ref-type="fig">Fig.8</xref>, <xref rid="f0045" ref-type="fig">Fig. 9</xref>, <xref rid="f0050" ref-type="fig">Fig. 10</xref>, <xref rid="f0055" ref-type="fig">Fig. 11</xref>, <xref rid="f0060" ref-type="fig">Fig. 12</xref>, <xref rid="f0065" ref-type="fig">Fig. 13</xref>, <xref rid="f0070" ref-type="fig">Fig. 14</xref>, <xref rid="f0075" ref-type="fig">Fig. 15</xref>, <xref rid="f0080" ref-type="fig">Fig. 16</xref>, <xref rid="f0085" ref-type="fig">Fig. 17</xref>).</p></list-item><list-item id="u0050"><label>•</label><p>The number of the square zones in features extraction equal to 4, 6 and 9 zones.</p></list-item><list-item id="u0055"><label>•</label><p>The number of the triangles zones in features extraction equal to 4, 6 and 8 zones.</p></list-item><list-item id="u0060"><label>•</label><p>Each numeral is transformed to a vector of 4, 6 and 9 components for square zoning and to a vector of 4, 6 and 8 components of triangular zoning in features extraction.</p></list-item><list-item id="u0065"><label>•</label><p>The standard deviation of the GRBF kernel function is equal to 0.1 in classification phase with support vectors machines.</p></list-item><list-item id="u0070"><label>•</label><p>The degree of the Polynomial (POL) kernel function is equal to 10 and their parameters <italic>a</italic>=<italic>b</italic>=1 in classification phase with support vectors machines.</p></list-item><list-item id="u0075"><label>•</label><p>We realized a variation on the size of the zones in features extraction to find the best performing method.</p></list-item><list-item id="u0080"><label>•</label><p>To do this, we have chosen the values {5, 10, 15} of hidden layer neurons number.</p></list-item></list></p><p>The graphical representation to recognition rate of each region <italic>τ</italic><sub>r</sub> is shown in <xref rid="f0025" ref-type="fig">Fig. 5</xref>.</p></sec></body><back><ref-list><title>References</title><ref id="bib1"><label>1</label><mixed-citation publication-type="other" id="othref0005">Institut Royal de la Culture Amazighe (IRCAM). Proposition de codification des tifinaghes, Rabat, Morocco, 2003a.</mixed-citation></ref><ref id="bib2"><label>2</label><mixed-citation publication-type="other" id="othref0010">Morocco in Figures 2003: a document by the Moroccan Embassy in the USA.</mixed-citation></ref><ref id="bib3"><label>3</label><element-citation publication-type="journal" id="sbref1"><person-group person-group-type="author"><name><surname>El Kessab</surname><given-names>B.</given-names></name><name><surname>Daoui</surname><given-names>C.</given-names></name><name><surname>Bouikhalene</surname><given-names>B.</given-names></name></person-group><article-title>Handwritten Tifinagh text recognition using neural networks and hidden Markov models</article-title><source>Int. J. Comput. Appl.</source><volume>75</volume><issue>18</issue><year>2013</year><fpage>975</fpage><lpage>8887</lpage></element-citation></ref><ref id="bib4"><label>4</label><element-citation publication-type="journal" id="sbref2"><person-group person-group-type="author"><name><surname>El Kessab</surname><given-names>B.</given-names></name><name><surname>Daoui</surname><given-names>C.</given-names></name><name><surname>Bouikhalene</surname><given-names>B.</given-names></name><name><surname>Salouan.</surname><given-names>R.</given-names></name></person-group><article-title>Some comparative studies or cursive handwritten Tifinagh characters recognition systems</article-title><source>Int. J. Hybrid Inform. Technol.</source><volume>7</volume><issue>6</issue><year>2014</year><fpage>295</fpage><lpage>306</lpage></element-citation></ref><ref id="bib5"><label>5</label><element-citation publication-type="book" id="sbref3"><person-group person-group-type="author"><name><surname>Sadiqi</surname><given-names>Fatima</given-names></name></person-group><chapter-title>The Teatchin of Tifinagh (Berber) in Morocco, Hand book of Language and Ethnic Identity, The Success-Failure Continuum in Language and Ethnic Identity Efforts</chapter-title><volume>Vol. 2</volume><year>2003</year><publisher-name>Oxford University Press</publisher-name><fpage>33</fpage><lpage>44</lpage></element-citation></ref><ref id="bib6"><label>6</label><mixed-citation publication-type="other" id="othref0015">K. Bentayebia, F. Abadaa, H. Ihzmadb, S. Amzazia, Genetic an cestry of a Moroccan population as inferred from autosomal STRs, Elsevier Meta Gene, 427–438.</mixed-citation></ref><ref id="bib7"><label>7</label><mixed-citation publication-type="other" id="othref0020">E. Katherine, Hoffman, Berber Language Ideologies, Maintenance, and Contraction: Gendered Variation in the Indigenous Margins of Morocco, Elsevier—Language &Communication, 144–167.</mixed-citation></ref></ref-list><sec id="s0015" sec-type="supplementary-material"><label>Appendix A</label><title>Supporting information</title><p><supplementary-material content-type="local-data" id="ec0005"><caption><p>Supplementary data</p></caption><media xlink:href="mmc1.zip"></media></supplementary-material></p></sec><fn-group><fn id="s0010" fn-type="supplementary-material"><label>Appendix A</label><p>Supplementary data associated with this article can be found in the online version at <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.1016/j.dib.2015.07.018" id="ir0005">doi:10.1016/j.dib.2015.07.018</ext-link>.</p></fn></fn-group></back><floats-group><fig id="f0005"><label>Fig. 1</label><caption><p>The proposed system for handwritten Tifinagh words recognition.</p></caption><graphic xlink:href="gr1"></graphic></fig><fig id="f0010"><label>Fig. 2</label><caption><p>Comparison between the Tifinagh, Arabic and Latin characters.</p></caption><graphic xlink:href="gr2"></graphic></fig><fig id="f0015"><label>Fig. 3</label><caption><p>Sixteen regions with Amazigh language.</p></caption><graphic xlink:href="gr3"></graphic></fig><fig id="f0020"><label>Fig. 4</label><caption><p>Example of handwritten Tifinagh region from the proposed data base.</p></caption><graphic xlink:href="gr4"></graphic></fig><fig id="f0025"><label>Fig. 5</label><caption><p>The graphical representation of recognition rate τr for each region.</p></caption><graphic xlink:href="gr5"></graphic></fig><fig id="f0030"><label>Fig. 6</label><caption><p>The original image.</p></caption><graphic xlink:href="gr6"></graphic></fig><fig id="f0035"><label>Fig. 7</label><caption><p>Graphical representation of segmentation column.</p></caption><graphic xlink:href="gr7"></graphic></fig><fig id="f0040"><label>Fig.8</label><caption><p>The segmentation in columns.</p></caption><graphic xlink:href="gr8"></graphic></fig><fig id="f0045"><label>Fig. 9</label><caption><p>The square zoning method.</p></caption><graphic xlink:href="gr9"></graphic></fig><fig id="f0050"><label>Fig. 10</label><caption><p>Processes of feature extraction by square zoning.</p></caption><graphic xlink:href="gr10"></graphic></fig><fig id="f0055"><label>Fig. 11</label><caption><p>The triangular zoning method.</p></caption><graphic xlink:href="gr11"></graphic></fig><fig id="f0060"><label>Fig. 12</label><caption><p>Processes of feature extraction by triangular zoning.</p></caption><graphic xlink:href="gr12"></graphic></fig><fig id="f0065"><label>Fig. 13</label><caption><p>The determination of optimal hyperplane, vectors supports, maximum Marge and valid hyperplanes.</p></caption><graphic xlink:href="gr13"></graphic></fig><fig id="f0070"><label>Fig. 14</label><caption><p>The multi-layer perceptron.</p></caption><graphic xlink:href="gr14"></graphic></fig><fig id="f0075"><label>Fig. 15</label><caption><p>The graphical representation of recognition rate <italic>τ</italic><sub>r</sub> of each region with all methods.</p></caption><graphic xlink:href="gr15"></graphic></fig><fig id="f0080"><label>Fig. 16</label><caption><p>The graphical representation of recognition rate <italic>τ</italic><sub>r</sub> of all feature extraction methods.</p></caption><graphic xlink:href="gr16"></graphic></fig><fig id="f0085"><label>Fig. 17</label><caption><p>The graphical representation of recognition rate <italic>τ</italic><sub>r</sub> of square zoning method and SVM classifier.</p></caption><graphic xlink:href="gr17"></graphic></fig><table-wrap id="t0005" position="float"><label>Table 1</label><caption><p>The obtained recognition rates <italic>τ</italic><sub>r</sub> and <italic>τ</italic><sub>g</sub> by each hybrid method and each classifier.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center"><bold>Regions</bold></th><th colspan="2" align="center"><bold>Neural networks</bold><hr></hr></th><th colspan="2" align="center"><bold>Support vectors machines</bold><hr></hr></th></tr><tr><th align="left"><bold>Square zoning</bold></th><th align="left"><bold>Triangular zoning</bold></th><th align="left"><bold>Square zoning</bold></th><th align="left"><bold>Triangular zoning</bold></th></tr></thead><tbody><tr><td align="center"><inline-graphic xlink:href="fx1.gif"></inline-graphic></td><td align="char">70.00</td><td align="char">67.57</td><td align="char">82.00</td><td align="char">74.49</td></tr><tr><td align="center"><inline-graphic xlink:href="fx2.gif"></inline-graphic></td><td align="char">79.13</td><td align="char">73.4</td><td align="char">80.00</td><td align="char">75.18</td></tr><tr><td align="center"><inline-graphic xlink:href="fx3.gif"></inline-graphic></td><td align="char">60.00</td><td align="char">60.74</td><td align="char">83.00</td><td align="char">80.34</td></tr><tr><td align="center"><inline-graphic xlink:href="fx4.gif"></inline-graphic></td><td align="char">55.09</td><td align="char">53.48</td><td align="char">76.67</td><td align="char">70.61</td></tr><tr><td align="center"><inline-graphic xlink:href="fx5.gif"></inline-graphic></td><td align="char">65.21</td><td align="char">64.00</td><td align="char">74.00</td><td align="char">70.78</td></tr><tr><td align="center"><inline-graphic xlink:href="fx6.gif"></inline-graphic></td><td align="char">63.25</td><td align="char">65.85</td><td align="char">69.00</td><td align="char">66.60</td></tr><tr><td align="center"><inline-graphic xlink:href="fx7.gif"></inline-graphic></td><td align="char">50.18</td><td align="char">50.97</td><td align="char">68.67</td><td align="char">65.00</td></tr><tr><td align="center"><inline-graphic xlink:href="fx8.gif"></inline-graphic></td><td align="char">69.66</td><td align="char">65.93</td><td align="char">71.67</td><td align="char">70.56</td></tr><tr><td align="center"><inline-graphic xlink:href="fx9.gif"></inline-graphic></td><td align="char">64.46</td><td align="char">61.71</td><td align="char">67.00</td><td align="char">63.60</td></tr><tr><td align="center"><inline-graphic xlink:href="fx10.gif"></inline-graphic></td><td align="char">71.31</td><td align="char">67.40</td><td align="char">72.00</td><td align="char">70.96</td></tr><tr><td align="center"><inline-graphic xlink:href="fx11.gif"></inline-graphic></td><td align="char">73.00</td><td align="char">71.43</td><td align="char">74.00</td><td align="char">71.73</td></tr><tr><td align="center"><inline-graphic xlink:href="fx12.gif"></inline-graphic></td><td align="char">66.11</td><td align="char">64.84</td><td align="char">67.57</td><td align="char">69.41</td></tr><tr><td align="center"><inline-graphic xlink:href="fx13.gif"></inline-graphic></td><td align="char">68.67</td><td align="char">62.69</td><td align="char">70.40</td><td align="char">69.00</td></tr><tr><td align="center"><inline-graphic xlink:href="fx14.gif"></inline-graphic></td><td align="char">73.37</td><td align="char">69.29</td><td align="char">72.74</td><td align="char">70.44</td></tr><tr><td align="center"><inline-graphic xlink:href="fx15.gif"></inline-graphic></td><td align="char">67.45</td><td align="char">66.04</td><td align="char">69.48</td><td align="char">61.33</td></tr><tr><td align="center"><inline-graphic xlink:href="fx16.gif"></inline-graphic></td><td align="char">69.26</td><td align="char">68.34</td><td align="char">81.4</td><td align="char">74.18</td></tr><tr><td align="center"><italic>τ</italic><sub><italic>g</italic></sub></td><td align="char">66.64</td><td align="char">64.61</td><td align="char">73.73</td><td align="char">70.26</td></tr></tbody></table><table-wrap-foot><fn><p>All values of the recognition rate for each region <italic>τ</italic><sub>r</sub> (given in %) and also those of the global rate recognition <italic>τ</italic><sub><italic>g</italic></sub> of all 16 regions (given in %) which we have obtained in the table.</p></fn></table-wrap-foot></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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| This area was generated with Dilib version V0.6.33. |  |