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Optical Character Recognition for Hindi Language Using a Neural-network Approach
Yadav, Divakar,Sanchez-Cuadrado, Sonia,Morato, Jorge Korea Information Processing Society 2013 Journal of information processing systems Vol.9 No.1
Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.
Julio Cesar Magalhães,Mariana Gongora,Renan Vicente,Juliana Bittencourt,Guaraci Tanaka,Bruna Velasques,Silmar Teixeira,Gledys Morato,Luis F. Basile,Oscar Arias-Carrión,Fernando A.M.S Pompeu,Mauricio C 대한정신약물학회 2015 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.13 No.1
Objective: The present study sought to analyze the influence of Levetiracetam (LEV) in cognitive performance by identifying the changes produced by LEV in reaction time, in neuropsychological assessment of attention and memory and in absolute theta power in frontal activity. Methods: Twelve healthy subjects (5 men and 7 women; mean age, 30.08 years, standard deviation, 4.71) were recruited for this study. The neuropsychological tests: Trail Making Test (A and B), Digit Span (direct and indirect numerical orders/working memory); Stroop test (inhibitory control of attention); Tower of London (planning and decision-making) and a quantitative electroencephalography were applied in 2 different days after and before the participants ingested the capsule of placebo or 500 mg LEV. Results: A two-way-ANOVA was implemented to observe the interaction between conditions (placebo or LEV 500 mg) and moments (pre- and post-ingestion of LEV or placebo). The data were analyzed by the SPSS statistical package (p<0.05). For the neuropsychological parameter, the Trail Making Test (A) was the only test that showed significant difference for condition in the task execution time (p=0.026). Regarding the reaction time in the behavioral parameter, an interaction between both factors (p=0.034) was identified through a two-way-ANOVA (condition versus moment). Electrophysiological measures showed a significant interaction for electrodes: F7, F3, and FZ. Conclusion: The findings showed that LEV promotes an important cognitive enhancement in the executive functions.