Segmentation of heterogeneous document images : an ... - Tel
Segmentation of heterogeneous document images : an ... - Tel Segmentation of heterogeneous document images : an ... - Tel
tel-00912566, version 1 - 2 Dec 2013 Figure 4.18: This figure displays the number of misclassified sites per iteration for 5 experiments with different maximum number of ICM cycles. trade-off between speed and accuracy of the training process. 4.9 Final Notes This chapter provided a method for detecting and separating regions or columns of text. Because we are yet to detect paragraphs within each region, a complete evaluation and comparison of the results will be performed in chapter 6, when other parts of the system are available. 86
Chapter 5 Text line detection tel-00912566, version 1 - 2 Dec 2013 Text line detection refers to the segmentation of each text region into distinct entities, namely text lines. In chapter 2 we mentioned and analyzed many methods for detecting text line. Our text line detection method is a variant of the method proposed by Papavassiliou in [75]. The original method segments a document image into non-overlapping vertical zones with equal width. The height of each zone is equal to the height of the document image. Its width is equal to 5% of the width of the document image so as to ignore the effect of skewed text lines, and wide enough to contain decent amount of characters. Also the original method disregards zones situated close to the left and right borders of the page; mainly because they do not contain sufficient amount of text. Since documents in our corpus contain side notes, it is not wise to dismiss zones that do not contain sufficient amount of text compared to zones in the middle of the document. One reason why the original method neglects these zones is because of the effect of large gaps that affect the overall estimation of model parameters. To solve this problem we ensure that parameters of the model are estimated from detected text regions. Also we ensure that detected lines do not cross from one text region to another as it happens in the original method. 5.1 Initial text line separators The first step is to calculate the projection profile of each vertical zone onto y axis. Let P R i be the projection profile of the i th vertical zone onto y axis. Peaks and valleys of PRs give rough indication of the location of text lines; however, in the case where writing style results in large gaps between successive words, a vertical zone many not contain enough foreground pixels for every text line. In order to slake the influence of these instances on P R i , a smoothed projection profile SP R i is estimated as a normalized weighted sum of M profiles on either side of the i th zone. The dimension for P R i and SP R i is 1 × Page’s height. In figures 5.1 and 5.2, the bar chart view for P R i and SP R i are rendered at the 87
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tel-00912566, version 1 - 2 Dec 2013<br />
Figure 4.18: This figure displays the number <strong>of</strong> misclassified sites per iteration for<br />
5 experiments with different maximum number <strong>of</strong> ICM cycles.<br />
trade-<strong>of</strong>f between speed <strong>an</strong>d accuracy <strong>of</strong> the training process.<br />
4.9 Final Notes<br />
This chapter provided a method for detecting <strong>an</strong>d separating regions or columns<br />
<strong>of</strong> text. Because we are yet to detect paragraphs within each region, a complete<br />
evaluation <strong>an</strong>d comparison <strong>of</strong> the results will be performed in chapter 6, when<br />
other parts <strong>of</strong> the system are available.<br />
86
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