Inventing a Recognition System to Rotate, Scale and Translate Invariant Characters
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Research Paper (undergraduate) from the year 2018 in the subject Engineering - Computer Engineering, grade: 9.8, University of the Punjab (Guru Nanak Dev Engineering College, Ludhiana), course: Masters of Technology, language: English, abstract: The traditional test extraction suffers from the drawback of size style and rotation of text arose on the images. Thus the scanning device needs to focus on the textual region of the images. Which is going to involve the person who is using the application software? This can be automated using algorithm which is written and designed in such a way so th...
Research Paper (undergraduate) from the year 2018 in the subject Engineering - Computer Engineering, grade: 9.8, University of the Punjab (Guru Nanak Dev Engineering College, Ludhiana), course: Masters of Technology, language: English, abstract: The traditional test extraction suffers from the drawback of size style and rotation of text arose on the images. Thus the scanning device needs to focus on the textual region of the images. Which is going to involve the person who is using the application software? This can be automated using algorithm which is written and designed in such a way so that the text area on the image will be easily identified either at some orientation or at variable sizes. In the presented work, the characters are segmented by using the pixel neighborhood technique and resized to a 32x32 block. The centre of gravity of the different characters is computed by using the first order moments. The contour of the pixel is extracted by means of Robert's operator. The radii from centre of gravity to contour pixel and are arranged in descending order. If the same character is rotated about its centre of gravity by some angle, the same radii are extracted and are arranged in descending order. It is observed that the first few radii are same for the same character if rotated at any angle. This gives the rotation invariant character recognition. Further, the characters are normalized with respect to size by dividing the radii by mean radius. The location invariance is obtained by use of centre of gravity. In the proposed algorithm, the different in-variances are considered into the features extraction process such that the normalization of characters is done in all respect. Once the features of different characters are set and are constant for the same object in either form, then that features can be used for character identification purposes.
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