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https://rda.sliit.lk/handle/123456789/2818
Title: | Converting high resolution multi-lingual printed document images in to editable text using image processing and artificial intelligence |
Authors: | Jayakody, A Premachandra, H. W. H Kawanaka, H |
Keywords: | Converting high resolution multi-lingual printed document editable text image processing images artificial intelligence |
Issue Date: | 21-Jun-2022 |
Publisher: | IEEE |
Citation: | H. W. H. Premachandra, A. Jayakody and H. Kawanaka, "Converting high resolution multi-lingual printed document images in to editable text using image processing and artificial intelligence," 2022 2nd International Conference on Image Processing and Robotics (ICIPRob), 2022, pp. 1-7, doi: 10.1109/ICIPRob54042.2022.9798739. |
Series/Report no.: | 2022 2nd International Conference on Image Processing and Robotics (ICIPRob); |
Abstract: | The optical character recognition technique is used to convert information, mainly printed or handwritten text in paper materials, into an electronic format that the computers can edit. According to the literature, there are few competent OCR systems for recognizing multilingual characters in the form of Sinhala and English characters together. The lack of an appropriate technology to recognize multilingual text still remains as a problem that the current research community must address, and it has been designated as the key problem for this study. The main goal of this research is to develop a multilingual character recognition system that uses character image geometry features and Artificial Neural Networks to recognize printed Sinhala and English scripts together. It is intended that the solution would be improved to cover three Sri Lanka’s most commonly spoken languages, with the addition of Tamil as a later upgrade. The primary technologies for this study were character geometry features and Artificial Neural Networks. At the moment almost an 85% of success rate has been achieved with a database containing around 800 images, which are divided into 46 characters (20 Sinhala and 26 English), and each character is represented in 20 different forms of character images. Recognition of text from printed bi-lingual documents is experimented by extracting individual character data from such printed text documents and feeding them to the system. |
URI: | http://rda.sliit.lk/handle/123456789/2818 |
ISSN: | 978-1-6654-0771-7 |
Appears in Collections: | Department of Computer Systems Engineering Research Papers - Dept of Computer Systems Engineering Research Papers - IEEE Research Papers - SLIIT Staff Publications |
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File | Description | Size | Format | |
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Converting_high_resolution_multi-lingual_printed_document_images_in_to_editable_text_using_image_processing_and_artificial_intelligence.pdf Until 2050-12-31 | 982.16 kB | Adobe PDF | View/Open Request a copy |
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