Topic
Optical character recognition
About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.
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28 Dec 2000
TL;DR: In this article, a digital video or still camera including optical character recognition and translator functions is used to translate text included in captured images, where the user can identify the desired text with the image.
Abstract: A digital video or still camera including optical character recognition and translator functions to translate text included in captured images. Cursor control allows the user to identify the desired text with the image. Translation is also possible on text included in images replayed from the camera's memory.
57 citations
TL;DR: A new vertical segmentation algorithm is proposed in which the segmentation points are located after thinning the word image to get the stroke width of a single pixel and high segmentation accuracy is found to be achieved.
57 citations
TL;DR: A more robust procedure is to follow up by using an optimization algorithm to refine the transformation by finding a transformation that matches a scanned image to the machine-readable document description that was used to print the original.
Abstract: Since optical character recognition systems often require very large amounts of training data for optimum performance, it is important to automate the process of finding ground truth character identities for document images. This is done by finding a transformation that matches a scanned image to the machine-readable document description that was used to print the original. Rather than depend on finding feature points, a more robust procedure is to follow up by using an optimization algorithm to refine the transformation. The function to optimize can be based on the character bounding boxes – it is not necessary to have access to the actual character shapes used when printing the original.
57 citations
TL;DR: This work devised K reference patterns, which are found by digital image processing, but used in an optical analog computer, and explain the concept of principal components.
Abstract: The essence of character recognition is a comparison between the unknown character and a set of reference patterns. Usually, these reference patterns are all possible characters themselves, the whole alphabet in the case of letter characters. Obviously, N analog measurements are highly redundant, since only K = log2N binary decisions are enough to identify one out of N characters. Therefore, we devised K reference patterns accordingly. These patterns, called principal components, are found by digital image processing, but used in an optical analog computer. We will explain the concept of principal components, and we will describe experiments with several optical character recognition systems, based on this concept.
57 citations
31 Aug 2005
TL;DR: This work proposes a novel algorithm that over-segments each word, and then removes extra breakpoints using knowledge of letter shapes, and annotates each detected letter with shape information, to be used for recognition in future work.
Abstract: We propose a novel algorithm for the segmentation and prerecognition of offline handwritten Arabic text. Our character segmentation method over-segments each word, and then removes extra breakpoints using knowledge of letter shapes. On a test set of 200 images, 92.3% of the segmentation points were detected correctly, with 5.1% instances of over-segmentation. The prerecognition component annotates each detected letter with shape information, to be used for recognition in future work.
57 citations