![]() The text will, however, retain direct formatting. ![]() Text retains direct formatting when it's copied… If the Normal style in the other document is Arial 11pt, then that's how your text will appear. So when you paste your text into the other document, it takes on the formatting of Normal style in that other document. Unless you've chosen otherwise, all your text is in style Normal. The issue is that Word doesn't think the formatting is changing, because Word doesn't think to itself "I'm copying some text in Times New Roman 12pt." It thinks "I'm copying text in style Normal" or "I'm copying text in style Body Text" or whatever. Text takes on the style of the recipient document Depending on the work you're doing, that might be easier to manage. If you're having trouble copying text from Document 1 to Document 2, try doing it the other way round.Ĭopy from Document 2 into Document 1. The text now appears as, say, Arial 11pt. When it arrives in the recipient document, the formatting changes. For example, you have some text in Times New Roman 12pt, and you copy it into another document. When you copy text, the format of the text can change. in Word 2007 and Word 2010, Normal style is defined as Calibri, 11pt, aligned left, with 1.15 line spacing within the paragraph, and 10pts spacing after the paragraph.in Microsoft Word 2002 and Word 2003 Normal style is defined as Times New Roman, 12pt, aligned left, with single spacing and no space before or after the paragraph.If you haven't done anything to change it, ![]() The default out-of-the-box style is Normal style. All text has an underlying style, even if you've never applied a style to any text. The formatting of all text in your Microsoft Word document depends on styles. Sometimes you copy text from one document to another, and the format of the text changes. When text is copied from one document to another, it retains direct formatting, but otherwise takes on the formatting of the style in the receiving document. Word thinks you're copying text in, say, Body Text style. You think you're copying Arial 10pt text. Format of text copied from another document We made our full code for scraping, image composition and training publicly available at. We find that (1) our dataset generation pipeline allows a successful transfer to real test images (Mask AP 86.2), (2) a very accurate image selection process - in contrast to human intuition - is not crucial and a broader category definition can help to bridge the domain gap, (3) the usage of blending methods is beneficial compared to simple copy-and-paste. For the evaluation we created a dataset of parcel photos that were annotated automatically. We present a case study for our dataset generation approach by considering parcel segmentation. Finally, the composition of the images is done by pasting the objects using four different blending methods. In the third step, we generate random arrangements of the object of interest and distractors on arbitrary backgrounds. We employ an object-agnostic background removal model and compare three different methods for image selection: Object-agnostic pre-processing, manual image selection and CNN-based image selection. This approach of image scraping and selection relaxes the need for a real-world domain-specific dataset that must be either publicly available or created for this purpose. Hence, image selection is necessary as a second step. We first scrape images for the objects of interest from popular image search engines and since we rely only on text-based queries the resulting data comprises a wide variety of images. In contrast to existing work, our pipeline covers every step from data acquisition to the final dataset. In this paper, we present a fully automated pipeline to generate a synthetic dataset for instance segmentation in four steps. For real-world applications, obtaining such a dataset is usually a tedious task. ![]() State-of-the-art approaches in computer vision heavily rely on sufficiently large training datasets.
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