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- W4206605354 abstract "Optical Character Recognition (OCR) is seen as a popular approach to convert the scanned document/textual data which is in the form an image to machine encoded text. This approach is now used as the foundation of Optical Mathematical Equation Reader (OMER). The OMER, unlike the conventional OCR, does not just recognise the English text but also is capable of recognising the mathematical symbols embedded in the text. OMER aims to open doors to new age learning for the visually impaired. The objective of the proposed work is to recognise and read printed English plain text and text containing mathematical equations. The proposed algorithm uses contouring and thresholding technique for character/symbol sorting. Convolution Neural Network (CNN) is built and trained for symbol and character recognition. The sorted data is saved in a separate folder and tested for different cases. The sorted data is then fed as input to CNN to get in a text file. The trained CNN efficiency was obtained to be 94.09%. The output in the text file is then converted to speech." @default.
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- W4206605354 date "2022-01-01" @default.
- W4206605354 modified "2023-09-25" @default.
- W4206605354 title "Development of Robust Optical Mathematical Equation Reader" @default.
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- W4206605354 doi "https://doi.org/10.1007/978-981-16-6407-6_42" @default.
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