Penyelenggaraan struktur penahan cerun rock shed: langkah mitigasi runtuhan tanah di Simpang Pulai - Blue Valley, Perak

Industri pembinaan merupakan industri yang sangat mencabar bukan sahaja di Malaysia malah di seluruh dunia yang merangkumi skop 3D dirty, difficult and dangerous. Industri ini juga meruapakan antara penyumbang terbesar KDNK iaitu sebanyak 7.4 peratus pada tahun 2016, walaupun industri ini antara penyumbang terbesar dari aspek keselamatan iaitu kemalangan (CIDB, 2017). Justeru itu, pihak yang bertanggungjawab seharusnya memandang serius mengenai masalah-masalah yang dihadapi supaya industri ini mampu bersaing di peringkat antarabangsa

Arabic Typed Text Recognition in Graphics Images (ATTR-GI)

While optical character recognition (OCR) techniques may perform well on standard text documents, their performance degrades significantly in graphics images. In standard scanned text documents OCR techniques enjoy a number of convenient assumptions such as clear backgrounds, standard fonts, predefined line orientation, page size, the start point of written. These assumptions are not true in graphics documents such as Arabic advertisements, personal cards, screenshot. Therefore, in such types of images, greater attention is required in the initial stage of detecting Arabic text regions in order for subsequent character recognition steps to be successful. Special features of Arabic alphabet characters introduce additional challenges which are not present in Latin alphabet characters. In this research we propose a new technique for automatically detecting text in graphics documents, and preparing them for OCR processing. Our detection approach is based on some mathematical measurements to know is it a text or not and to know is it Arabic Based Text or Latin Based. These measurements are follows, measure the Base Line (the line has maximum number of black pixels). Also, measure Item Area (the content of extracted sub images). Finally, find maximum peak for the adjacent black pixels in Base line and maximum length for sub adjacent black pixels. Our experiment results will come in more details. We believe our technique will enable OCR systems to overcome their major shortcoming when dealing with text in graphics images. This will further enable a variety of OCR-based applications to extend their operation to graphics documents such as SPAM detection from image, reading advertisement for blind people, search and index document which contain image, enhancing for printer property (black white or color printer) and enhancing OCR

Bangla character recognition system is developed by using automatic feature extraction and XOR operation

This paper presents off-line bangle character recognition system using automatic feature extraction and XOR operation. In this system, the Bangla text is accepted as an image file which is first segmented into lines and words and then each word is segmented into characters. The pixels outside the boundary of the character are eliminated. The characters are scaled to a size equal to the database image. A XOR operation is performed between the scaled image and the database image and the error (%) is calculated. Finally, depending on the minimum error, the system recognizes the character to use in the output. The average recognition accuracy rate of the system was about 80%

A new approach for centerline extraction in handwritten strokes: an application to the constitution of a code book

International audienceWe present in this paper a new method of analysis and decomposition of handwritten documents into glyphs (graphemes) and their associated code book. The different techniques that are involved in this paper are inspired by image processing methods in a large sense and mathematical models implying graph coloring. Our approaches provide firstly a rapid and detailed characterization of handwritten shapes based on dynamic tracking of the handwriting (curvature, thickness, direction, etc.) and also a very efficient analysis method for the categorization of basic shapes (graphemes). The tools that we have produced enable paleographers to study quickly and more accurately a large volume of manuscripts and to extract a large number of characteristics that are specific to an individual or an era

Processing Pre-Existing Connect-The-Dots Puzzles For Educational Repurposing Applications

Connect-the-Dots puzzles are puzzles which contain labeled dots in a sequence. These puzzles are mostly designed as a way for children to hone in on their counting skills, while having fun. These same puzzles, which are available in abundance online and with modification, can be used to aid students in other areas of education such as spelling. Research shows that the addition of visual imagery provides a significant impact in spelling performance. The objective of this research is to develop an algorithm for processing Connect-the-Dots puzzles to assist in the replacement of the original numbers in the puzzle with characters that will help to facilitate an alternative educational purpose. In particular, the use of Optical Character Recognition (OCR) and image processing algorithms to process pre-existing Connect-the-Dots puzzles is explored. An algorithm was developed to locate and identify the numbers in the puzzles. The system is comprised of five components, namely, an Image Preprocessing component, a Dot Locator component, a Number Locator component, a Number Recognition component, and a Post-Processing component. To test the accuracy of the algorithm an experiment was conducted using 20 hand selected puzzles from an online source. The accuracy of the algorithm was evaluated, component by component, as well as overall, by visually capturing the make-up of the puzzles and comparing them to the results generated by the algorithm. Results show that the algorithm performed at an overall accuracy rate of 66%. However, the Dot Locator component performed at a rate of 100%, the Number Locator at a rate of 86%, and the Number Recognition at a rate of 76%. This research will aid in the development of an application that may provide educational benefits to children who are exposed to using technology for learning, at a young age

Processing Pre-Existing Connect-The-Dots Puzzles For Educational Repurposing Applications

Connect-the-Dots puzzles are puzzles which contain labeled dots in a sequence. These puzzles are mostly designed as a way for children to hone in on their counting skills, while having fun. These same puzzles, which are available in abundance online and with modification, can be used to aid students in other areas of education such as spelling. Research shows that the addition of visual imagery provides a significant impact in spelling performance. The objective of this research is to develop an algorithm for processing Connect-the-Dots puzzles to assist in the replacement of the original numbers in the puzzle with characters that will help to facilitate an alternative educational purpose. In particular, the use of Optical Character Recognition (OCR) and image processing algorithms to process pre-existing Connect-the-Dots puzzles is explored. An algorithm was developed to locate and identify the numbers in the puzzles. The system is comprised of five components, namely, an Image Preprocessing component, a Dot Locator component, a Number Locator component, a Number Recognition component, and a Post Processing component. To test the accuracy of the algorithm an experiment was conducted using 20 hand selected puzzles from an online source. The accuracy of the algorithm was evaluated, component by component, as well as overall, by visually capturing the make-up of the puzzles and comparing them to the results generated by the algorithm. Results show that the algorithm performed at an overall accuracy rate of 66%. However, the Dot Locator component performed at a rate of 100%, the Number Locator at a rate of 86%, and the Number Recognition at a rate of 76%. This research will aid in the development of an application that may provide educational benefits to children who are exposed to using technology for learning, at a young age