Topic
Document processing
About: Document processing is a research topic. Over the lifetime, 4174 publications have been published within this topic receiving 65885 citations.
Papers published on a yearly basis
Papers
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01 Jan 1971
2,877 citations
31 Dec 1994
TL;DR: An N-gram-based approach to text categorization that is tolerant of textual errors is described, which worked very well for language classification and worked reasonably well for classifying articles from a number of different computer-oriented newsgroups according to subject.
Abstract: Text categorization is a fundamental task in document processing, allowing the automated handling of enormous streams of documents in electronic form. One difficulty in handling some classes of documents is the presence of different kinds of textual errors, such as spelling and grammatical errors in email, and character recognition errors in documents that come through OCR. Text categorization must work reliably on all input, and thus must tolerate some level of these kinds of problems. We describe here an N-gram-based approach to text categorization that is tolerant of textual errors. The system is small, fast and robust. This system worked very well for language classification, achieving in one test a 99.8% correct classification rate on Usenet newsgroup articles written in different languages. The system also worked reasonably well for classifying articles from a number of different computer-oriented newsgroups according to subject, achieving as high as an 80% correct classification rate. There are also several obvious directions for improving the system`s classification performance in those cases where it did not do as well. The system is based on calculating and comparing profiles of N-gram frequencies. First, we use the system to compute profiles on training set data that represent the variousmore » categories, e.g., language samples or newsgroup content samples. Then the system computes a profile for a particular document that is to be classified. Finally, the system computes a distance measure between the document`s profile and each of the category profiles. The system selects the category whose profile has the smallest distance to the document`s profile. The profiles involved are quite small, typically 10K bytes for a category training set, and less than 4K bytes for an individual document. Using N-gram frequency profiles provides a simple and reliable way to categorize documents in a wide range of classification tasks.« less
1,826 citations
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TL;DR: “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research.
Abstract: In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research.
1,626 citations
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01 Nov 1995TL;DR: A pen-like instrument with a writing point for making written entries upon a physical document and sensing the three-dimensional forces exerted on the writing tip as well as the motion associated with the act of writing is described in this article.
Abstract: A manual entry interactive paper and electronic document handling and process system uses a pen-like instrument (PI) with a writing point for making written entries upon a physical document and sensing the three-dimensional forces exerted on the writing tip as well as the motion associated with the act of writing. The PI is also equipped with a CCD array for reading pre-printed bar codes used for identifying document pages and other application defined areas on the page, as well as for providing optical character recognition data. A communication link between the PI and an associated base unit transfers the transducer data from the PI. The base unit includes a programmable processor, a display, and a communication link receiver. The processor includes programs for written character and word recognition, memory for storage of an electronic version of the physical document and any hand-written additions to the document. The display unit displays the corresponding electronic version of the physical document on a CRT or LCD as a means of feedback to the user and for use by authorized electronic agents.
1,024 citations
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TL;DR: The document spectrum (or docstrum) as discussed by the authors is a method for structural page layout analysis based on bottom-up, nearest-neighbor clustering of page components, which yields an accurate measure of skew, within-line, and between-line spacings and locates text lines and text blocks.
Abstract: Page layout analysis is a document processing technique used to determine the format of a page. This paper describes the document spectrum (or docstrum), which is a method for structural page layout analysis based on bottom-up, nearest-neighbor clustering of page components. The method yields an accurate measure of skew, within-line, and between-line spacings and locates text lines and text blocks. It is advantageous over many other methods in three main ways: independence from skew angle, independence from different text spacings, and the ability to process local regions of different text orientations within the same image. Results of the method shown for several different page formats and for randomly oriented subpages on the same image illustrate the versatility of the method. We also discuss the differences, advantages, and disadvantages of the docstrum with respect to other lay-out methods. >
654 citations