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Koios++: A Query-Answering System for Handwritten Input
"... Abstract-In this paper we propose KOIOS++, which automatically processes natural language queries provided by handwritten input. The system integrates several recent achievements in the area of handwriting recognition, natural language processing, information retrieval, and human computer interacti ..."
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Abstract-In this paper we propose KOIOS++, which automatically processes natural language queries provided by handwritten input. The system integrates several recent achievements in the area of handwriting recognition, natural language processing, information retrieval, and human computer
Benefits of Handwritten Input for Students Learning Algebra Equation Solving
- In Proceedings of the Int’l Conf. on Artificial Intelligence in Education (AIEd’07
, 2007
"... Abstract. Building on past results establishing a benefit for using handwriting ..."
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Cited by 8 (6 self)
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Abstract. Building on past results establishing a benefit for using handwriting
How Handwritten Input Helps Students Learning Algebra Equation Solving
, 2008
"... Building on past results establishing a benefit for using handwriting when entering mathematics on the computer, we hypothesize that handwriting as an input modality may be able to provide significant advantages over typing in the mathematics learning domain. The use of handwriting may result in dec ..."
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Cited by 2 (1 self)
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Building on past results establishing a benefit for using handwriting when entering mathematics on the computer, we hypothesize that handwriting as an input modality may be able to provide significant advantages over typing in the mathematics learning domain. The use of handwriting may result
Handwritten Digit Recognition with a Back-Propagation Network
- Advances in Neural Information Processing Systems
, 1990
"... We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated d ..."
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Cited by 285 (21 self)
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We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated
On the application of text input metrics to handwritten text input
"... This paper describes the current metrics used in text input research, considering those used for discrete text input as well as those used for spoken input. It examines how these metrics might be used for handwritten text input and provides some thoughts about different metrics that might allow for ..."
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This paper describes the current metrics used in text input research, considering those used for discrete text input as well as those used for spoken input. It examines how these metrics might be used for handwritten text input and provides some thoughts about different metrics that might allow
Use of Splines in Handwritten Character Recognition
"... Abstract — Handwritten Character Recognition is software used to identify the handwritten characters and receive and interpret intelligible handwritten input from sources such as manuscript documents. The recent past several years has seen the development of many systems which are able to simulate t ..."
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Abstract — Handwritten Character Recognition is software used to identify the handwritten characters and receive and interpret intelligible handwritten input from sources such as manuscript documents. The recent past several years has seen the development of many systems which are able to simulate
A lexicon driven approach to handwritten word recognition for real-time applications
- IEEE Transactions on PAMI
, 1997
"... Abstract—A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and ..."
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Cited by 117 (31 self)
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Abstract—A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry
Input Fuzzy Modeling for the Recognition of Handwritten Hindi Numerals
- In: International Conference on Information Technology. 45 Journal of Computer Applications (0975 – 8887) Volume 70– No.19
, 2007
"... This paper presents the recognition of Handwritten Hindi Numerals based on the modified exponential membership function fitted to the fuzzy sets derived from normalized distance features obtained using the Box approach. The exponential membership function is modified by two structural parameters tha ..."
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Cited by 3 (0 self)
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This paper presents the recognition of Handwritten Hindi Numerals based on the modified exponential membership function fitted to the fuzzy sets derived from normalized distance features obtained using the Box approach. The exponential membership function is modified by two structural parameters
Symbol Recognition In Handwritten Mathematical Formulas
, 1994
"... : In this paper an efficient on-line recognition system for handwritten mathematical formulas is proposed. After formula preprocessing a symbol hypotheses net is generated by extracting different features for stroke unity. Each element of the symbol hypotheses net represents a possible symbol. The ..."
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of the handwritten input is done by calculating the best fitting path through the symbol hypotheses net under regard of the stroke group probabilities and the probabilities obtained by the HMM symbol recognizer. 1 INTRODUCTION Automatic recognition of handwritten mathematical numerals and characters is a subject
Efficient learning of sparse representations with an energy-based model
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (NIPS 2006
, 2006
"... We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying non-linearity that turns a code vector into a quasi-binary sparse code vector. Given an input, the optimal code minimizes the distance b ..."
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Cited by 219 (15 self)
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We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying non-linearity that turns a code vector into a quasi-binary sparse code vector. Given an input, the optimal code minimizes the distance
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