On-line Text Recognition Research Articles

DAD: A Detailed Arabic Dataset for Online Text Recognition and Writer Identification, a New Type

This paper presents a novel Arabic dataset that considers the characteristics of the Arabic language filling some gaps not covered by existing datasets. Conventional datasets consider Arabic in a similar way to Latin languages. These datasets either delete diacritic and supplement marks, considering them as defects, or keep them without considering the actual meaning. More than half of all Arabic characters have diacritics above or below characters. In this context, this work presents the novel Detailed Arabic Dataset (DAD) for bridging these gaps. The additional marks included in this dataset are the single dot, two dots -, three dots ^, Hamza and two supplement marks: The bar for Tah, or Zah and the complement bar for Kaf. A special application was built to generate a dataset for Arabic online recognition and writer identification (called OFMArabicDatasetBuilder). Totally the ground truth contains 93064 entries based on sub-word and letter parts (not on words or lines as other datasets). This dataset will provide researchers with a strong tool for online Arabic language text recognition especially in the segmentation phase and writer identification. This paper also presents benchmarking results of using k-nearest neighbours machine learning with DAD.

Online Recognition Approach of Arabic Numerals Using Matching Alignment Algorithm

Online text recognition systems have been continually given due importance these days globally because of the rapidly developing touch screen gadgets. However, it has been difficult to utilize keyboards and external mouse-like inputs in significantly tinier devices which consequently paved the way to current researched based scientists to look for some newer techniques which could design such type of online systems which could further deal with different kinds of texts for example, digits, symbols and alphabets. In the present paper, an online system for recognizing manually written Arabic numerals is being given. This paper will show digit acquisition, preprocessing, feature extraction and recognition phases in detail. The set of the data was gathered from 100 writers using a touch screen PC with 100 samples of every digit. The average accuracy rate of the outcome of the test of this proposed system was 98%, which is a significant accuracy rate.