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In this paper, a font size independent Optical Character Recognition (OCR) system for Urdu document images is presented. Urdu documents are written using Noori Nastalique writing style with different font sizes of normal text and headings. Most of current state of the art techniques of Urdu OCRs support recognition of text having single font size. The presented study deals with the recognition of...
In recent years, with the widespread of Internet and digitized processing of multi-script documents worldwide, script identification techniques have become more important in the pattern recognition field. Script identification concerns methods for identifying different scripts in multi-lingual, multi-script documents. This paper presents a comprehensive overview on research activities in the field...
In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that combined confidence-based backoff strategies can produce more accurate results than each technique produces by itself and even the ones exhibited by the...
Optical Character Recognition (OCR) is one of key research areas of Artificial Intelligence (AI), and image text recognition is one of challenging fields of OCR. Presented work offers a character recognition system for cursive script (e.g., Arabic, Urdu, etc.) segmented characters from their images. Presented methodology consists of phases namely (1) Image Acquisition, (2) Preprocessing, (3) Chain...
Japanese language has complex writing systems, Kanji and Kana (Katakana and Hiragana). Each one has different style of writing. One simple way to differentiate is Kanji have more strokes than Kana. Meanwhile, it needs a lot of effort to remember characters of Katakana and Hiragana, thus it will be very difficult to distinguish handwritten Katakana and Hiragana, since there are a lot of similar characters...
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