Proceedings Article10.1109/ICDAR.1995.598949
Reading handwritten US census forms
Sriganesh Madhvanath,Venu Govindaraju,V. Ramanaprasad,Dar-Shyang Lee,Sargur N. Srihari +4 more
- 14 Aug 1995
- Vol. 1, pp 82-85
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TL;DR: The approach taken by CEDAR to automate the task of reading the census forms is discussed and the subtasks of field extraction and phrase recognition are described.
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Abstract: Commercial forms-reading systems for extraction of data from forms do not meet acceptable accuracy requirements on forms filled out by hand. In December 1993, NIST called industry and research organizations working in the area of handwriting recognition to participate in a test to determine the state of the art in the area. A database of form images containing actual responses received by the US Census Bureau was provided. The handwritten responses are very loosely constrained in terms of writing style, format of response and choice of text. The sizes of the lexicons provided are very large (about 50000 entries) and yet the coverage is incomplete (about 70%). In this paper we discuss the approach taken by CEDAR to automate the task of reading the census forms. The subtasks of field extraction and phrase recognition are described.
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Citations
Handwritten CAPTCHA: using the difference in the abilities of humans and machines in reading handwritten words
Amalia Rusu,Venu Govindaraju +1 more
- 26 Oct 2004
TL;DR: The application of human interactive proofs (HIP), which is a relatively new research area with the primary focus of defending online services against abusive attacks, uses a set of security protocols based on automatic tests that humans can pass but the state-of-the-art computer programs cannot.
Off-line Bangla handwritten word recognition: a holistic approach
TL;DR: A holistic handwritten word recognition method is developed using a feature descriptor, designed by combining different Elliptical, Tetragonal and Vertical pixel density histogram-based features, which performs comparatively better with SVM than MLP for the prepared dataset.
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A holistic approach for Off-line handwritten cursive word recognition using directional feature based on Arnold transform
TL;DR: This paper presents a holistic off-line handwriting recognition system based on extraction of directional features which depends on the stroke orientation distribution of cursive word, which is compared with the state-of-the-art methods for handwritten character recognition using C-Cube data-set.
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•Dissertation
Automatic recognition of handwritten medical forms for search engines
Robert Jay Milewski
- 01 Jan 2006
TL;DR: It is shown that a few recognized characters, returned by handwriting recognition, can be used to construct a linguistic model capable of representing a medical topic category, thereby improving handwriting recognition performance and allowing PCR (Pre-Hospital Care Report) forms to be tagged with a topic category and subsequently searched by information retrieval systems.
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Probabilistic model for segmentation based word recognition with lexicon
Sergey Tulyakov,Venu Govindaraju +1 more
- 01 Sep 2001
TL;DR: The construction of a model for off-line word recognizers based on over-segmentation of the input image and recognition of segment combinations as characters in a given lexicon word is described.
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References
Interpretation of handwritten addresses in US mailstream
Sargur N. Srihari,Venu Govindaraju,A. Shekhawat +2 more
- 20 Oct 1993
TL;DR: The methodology uses diverse pattern recognition techniques, image processing algorithms (thresholding, underline removal, separation of lines, location and recognition of address components), and access to United States Postal Service databases to determine the DPC.
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•Proceedings Article
Incorporating Syntactic Constraints in Recognizing Handwritten Sentences.
Rohini K. Srihari,Charlotte M. Baltus +1 more
- 01 Jan 1993
TL;DR: Two statistical methods of applying syntactic constraints to the output of an HWR on input consisting of sentences/phrases based on syntactic categories (tags) associated with words are discussed.
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Handwritten Text Recognition
Tanishq Rampure,Harsh Damahe,Kundan Bharati,Darshil Raj,Namrata Naikwade +4 more
TL;DR: Handwritten text recognition involves the ability of a computer system to interpret and understand handwritten input. It involves categorizing and interpreting handwritten text. This study proposes a novel approach using deep neural networks and image segmentation to improve the accuracy and efficiency of handwritten text recognition.
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