9,173 research outputs found
Simple identification tools in FishBase
Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further
development. It explores the possibility of a holistic and integrated computeraided strategy
PUBLIC OCR SIGN AGE RECOGNITION WITH SKEW & SLANT CORRECTION FOR VISUALLY IMP AIRED PEOPLE
This paper presents an OCR hybrid recognition model for the Visually Impaired People
(VIP). The VIP often encounters problems navigating around independently because they are
blind or have poor vision. They are always being discriminated due to their limitation which can
lead to depression to the VIP. Thus, they require an efficient technological assistance to help
them in their daily activity. The objective of this paper is to propose a hybrid model for Optical
Character Recognition (OCR) to detect and correct skewed and slanted character of public
signage. The proposed hybrid model should be able to integrate with speech synthesizer for VIP
signage recognition. The proposed hybrid model will capture an image of a public signage to be
converted into machine readable text in a text file. The text will then be read by a speech
synthesizer and translated to voice as the output. In the paper, hybrid model which consist of
Canny Method, Hough Transformation and Shearing Transformation are used to detect and
correct skewed and slanted images. An experiment was conducted to test the hybrid model
performance on 5 blind folded subjects. The OCR hybrid recognition model has successfully
achieved a Recognition Rate (RR) of 82. 7%. This concept of public signage recognition is being
proven by the proposed hybrid model which integrates OCR and speech synthesizer
Handwritten Devanagari numeral recognition
Optical character recognition (OCR) plays a very vital role in today’s modern world. OCR can be useful for solving many complex problems and thus making human’s job easier. In OCR we give a scanned digital image or handwritten text as the input to the system. OCR can be used in postal department for sorting of the mails and in other offices. Much work has been done for English alphabets but now a day’s Indian script is an active area of interest for the researchers. Devanagari is on such Indian script. Research is going on for the recognition of alphabets but much less concentration is given on numerals. Here an attempt was made for the recognition of Devanagari numerals. The main part of any OCR system is the feature extraction part because more the features extracted more is the accuracy. Here two methods were used for the process of feature extraction. One of the method was moment based method. There are many moment based methods but we have preferred the Tchebichef moment. Tchebichef moment was preferred because of its better image representation capability. The second method was based on the contour curvature. Contour is a very important boundary feature used for finding similarity between shapes. After the process of feature extraction, the extracted feature has to be classified and for the same Artificial Neural Network (ANN) was used. There are many classifier but we preferred ANN because it is easy to handle and less error prone and apart from that its accuracy is much higher compared to other classifier. The classification was done individually with the two extracted features and finally the features were cascaded to increase the accuracy
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