[Andalus Italic Font Download
Abdul Soumphonphakdy
This view provides an easy way to apply properties to styles, with format options available from a toolbar and dialogs (similar to the way one would use an interface such as Microsoft Word). In some cases, only the most common property options are available in the Simplified view (e.g., font, letter/word spacing, paragraph alignment/indentation, autonumbering format, borders, background). One advantage of the Simplified view is that you can apply a property to multiple styles at the same time. You can also click a check box to hide the properties in the editor, allowing you to see only the styles.
For the properties, you can toggle between a grouped display and an alphabetical display. The Advanced view of the Stylesheet Editor lets you edit more settings than are available in the Simplified view. In addition, the Advanced view lets you see and apply settings to multiple mediums and media queries at the same time.
In the local toolbar of the editor, click . The Properties dialog opens. Select the Font tab. Click the down arrow in the Style field and select Bold (or Bold Italic if you want the text to also be italicized). In the Properties dialog, click OK.
Available in LabVIEW 2018 and later. The Ctrl-B, Ctrl-I, and Ctrl-U shortcuts now bold, italicize, and underline text, respectively. This behavior only occurs while a text field is being edited. Otherwise, these keyboard shortcuts maintain their normal behavior.
It would be nice if you could change a selected font style by using the standard windows (MS office) shortcuts, such as CTRL-B for bold and CTRL-U for underline. This would save many mouse clicks.
I'm really surprised this idea hasn't received more kudos. One of the *first* things I noticed when I started programming with LabVIEW almost 11 years ago was the lack of Ctrl-B/I/U keystrokes to bold/italicize/underline selected text. Come on, people! Kudo this one!
If you were mucking about with something like ctrl+d, which I pretty much never use and which few people it seems knows exist, I'd be fine. But you're suggesting changing three of the shortcuts I use most, and there aren't any other keys still availble. What operations would lose their shortcut?
I like the idea of standard text formatting shortcuts, don't get me wrong. But something just seems fishy about having different context actions for the same shortcut. The documentation on the LabVIEW Quick Reference Card would become a little more squirelly, "This means this here, but this means that there...." which can be a fundamental UI SNAFU.
Yeah, I read that. Didn't like it so much... it wouldn't save me much time. I am rarely using LV as a text editor. Usually I'm selecting all the controls on my FP and changing the font of everything, and a ctrl+b to make everything bold face would actually be useful when I'm trying to magnify a front panel for display in a presentation.
The following fonts are included in Microsoft Windows XP. They may not all be installed. The fonts in italics are not part of a default English install, as far as I know at least. Installed fonts can be found in the Windows\Fonts folder.
The aim of this paper is to experimentally evaluate the effectiveness of the state-of-the-art printed Arabic text recognition systems to determine open areas for future improvements. In addition, this paper proposes a standard protocol with a set of metrics for measuring the effectiveness of Arabic optical character recognition (OCR) systems to assist researchers in comparing different Arabic OCR approaches.
This paper describes an experiment to automatically evaluate four well-known Arabic OCR systems using a set of performance metrics. The evaluation experiment is conducted on a publicly available printed Arabic dataset comprising 240 text images with a variety of resolution levels, font types, font styles and font sizes.
Optical character recognition (OCR) is a technique that aims to automatically convert a machine-printed or handwritten text image into an editable text format (Alghamdi et al., 2016). This technique is highly desirable in various real-world applications, such as digitising learning resources to assist visually impaired people, bank cheque processing and mail sorting (Alginahi, 2013; Al-Badr and Mahmoud, 1995). Generally, the process for developing OCR systems involves five stages: pre-processing, segmentation, feature extraction, classification and post-processing. In each stage, specific techniques are applied; for more details, see Khorsheed (2002).
Previous research on text recognition has focused primarily on Latin scripts, such as English and Chinese, and it has not been until the last two decades that recognition of non-Latin scripts, such as Arabic, have been researched (Alginahi, 2013). Although handwritten script is significantly more challenging than printed Arabic text for OCR, Arabic printed text OCR still poses significant challenges (Alghamdi et al., 2016). Therefore, this study will deal only with Arabic printed text. Figure 1 illustrates the characteristics of Arabic printed text that contribute to the inadequate development of Arabic script recognition. Arabic script is written cursively through a baseline and contains loop-shaped characters, zigzag-shaped characters, dot characters and diacritics. Moreover, a character might have up to four different shapes in relation to its position in a word. Therefore, research is being undertaken seeking more solutions for Arabic OCR systems (Parvez and Mahmoud, 2013; Slimane et al., 2013).
To produce an efficient Arabic OCR system, effective performance evaluation of current OCR systems is essential. Furthermore, evaluating OCR performance contributes to monitoring progress in OCR system development, analysing the effectiveness of OCR systems, identifying open areas and providing a scientific explanation for the performance of OCR systems (Kanungo et al., 1999a; Mihov et al., 2005).
Moreover, the performance assessments of Arabic OCR systems are only reported by their developers: their results are derived from different datasets that might be small or might be used in developing the systems (Al-Badr and Mahmoud, 1995; Alginahi, 2013). Consequently, as these performance tests are statistically invalid, they cannot be used to compare the performance between Arabic OCR systems (Margner and El Abed, 2009). Evaluating the performance of Arabic OCR systems is also challenging as no standard dataset is available nor is a set of performance metrics freely available to the community of Arabic OCR developers (Al-Muhtaseb and Qahwaji, 2011; Abdelraouf et al., 2008; Al-Badr and Mahmoud, 1995; Ahmad et al., 2016). In addition, most reports on the performance of Arabic OCR systems are in terms of the general, standard performance measurement of character accuracy, such as in Dahi et al. (2015) and Ahmad et al. (2016). However, this performance metric is insufficient to assess how Arabic OCR systems are coping with the challenges of Arabic script.
Thus, the current work first attempts to provide a better insight into the effectiveness of the state-of-the-art printed Arabic OCR systems with possible interpretations for future performance enhancement. It then aims to propose a standard protocol with a set of metrics for measuring the effectiveness of Arabic OCR systems which we hope will be used as a benchmark by researchers in comparing between OCR algorithms.
This paper is organised as follows: in the first section below, the most common Arabic OCR systems are introduced. The Arabic OCR system evaluation background and performance metrics for Arabic OCR system evaluation are discussed in the second and third sections, respectively. An experimental protocol is presented in the fourth section. The experimental results are then presented and discussed. In the final section, future work is suggested and the conclusion is presented.
Only a handful of OCR systems claim that they are capable of recognising Arabic script. Our evaluation study is limited to the four most well-known Arabic OCR systems, namely, Automatic Reader 11.2 produced by the Sakhr Software Company; FineReader 12 produced by the ABBYY Company; Clever Page produced by RDI (Research & Development International) and Tesseract produced originally by Hewlett-Packard (HP). In the following subsections, the four Arabic OCR engines are briefly discussed.
Automatic Reader is a commercial product first developed by the Sakhr Software Company in 1982 for text recognition of Arabic script. It supports Arabic language and several Arabic character-based languages, such as Arabic, Farsi and Urdu. Sakhr claims that Automatic Reader has been ranked as the best existing Arabic OCR software for high-quality text images by US government evaluators (Sakhr Software OCR, 2017). It supports multi-font type and multi-resolution images. However, font size 8 is not supported by the Automatic Reader OCR software (henceforth, referred to as Sahkr OCR).
FineReader is produced commercially by a global company, called ABBYY, as advanced OCR software. The performance of FineReader has been enhanced by ABBYY for many years. FineReader 12 supports 190 languages including Arabic script using dictionary support (Abbyy OCR, 2017). It supports multi-font types, multi-size and multi-resolution images. Henceforth, FineReader will be referred to as ABBYY OCR.
Clever Page originally began as a PhD research study by El-Mahallawy (2008). Since 2008, Clever Page has been designed and developed as an Omni font-written Arabic OCR engine by Research & Development International (RDI). It supports multi-font types and multi-size text images. However, it is worth mentioning that the Clever Page OCR software only works on pages with 300 dots per inch (dpi). Henceforth, Clever Page will be referred to as RDI OCR.
Tesseract is an OCR engine designed at Hewlett-Packard (HP) between 1984 and 1994. Since late 2005, it has been maintained by Google and released as open source OCR software. However, Arabic support has only been added recently (Sabbour and Shafait, 2013). Tesseract is the only Arabic OCR software that is freely available. It supports multi-font types, and multi-size and multi-resolution images.
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