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- W2104626419 abstract "Cheap and versatile cameras make it possible to easily and quickly capture a wide variety of documents. However, low resolution cameras present a challenge to OCR because it is virtually impossible to do character segmentation independently from recognition. In this paper we solve these problems simultaneously by applying methods borrowed from cursive handwriting recognition. To achieve maximum robustness, we use a machine learning approach based on a convolutional neural network. When our system is combined with a language model using dynamic programming, the overall performance is in the vicinity of 80-95% word accuracy on pages captured with a 1024/spl times/768 webcam and 10-point text." @default.
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- W2104626419 date "2005-01-01" @default.
- W2104626419 modified "2023-09-27" @default.
- W2104626419 title "Text recognition of low-resolution document images" @default.
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- W2104626419 doi "https://doi.org/10.1109/icdar.2005.233" @default.
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