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The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System

Neural Information Processing Systems

This system combines a robust input representation, which preserves the dynamic writing information, with a neural network architecture, a so called Multi-State Time Delay Neural Network (MS-TDNN), which integrates rec.ognition and segmentation in a single framework. Our preprocessing transforms the original coordinate sequence into a (still temporal) sequence offeature vectors, which combine strictly local features, like curvature or writing direction, with a bitmap-like representation of the coordinate's proximity. The MS-TDNN architecture is well suited for handling temporal sequences as provided by this input representation. Our system is tested both on writer dependent and writer independent tasks with vocabulary sizes ranging from 400 up to 20,000 words. For example, on a 20,000 word vocabulary we achieve word recognition rates up to 88.9% (writer dependent) and 84.1 % (writer independent) without using any language models.


The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System

Neural Information Processing Systems

This system combines a robust input representation, which preserves the dynamic writing information, with a neural network architecture, a so called Multi-State Time Delay Neural Network (MS-TDNN), which integrates rec.ognition and segmentation in a single framework. Our preprocessing transforms the original coordinate sequence into a (still temporal) sequence offeature vectors, which combine strictly local features, like curvature or writing direction, with a bitmap-like representation of the coordinate's proximity. The MS-TDNN architecture is well suited for handling temporal sequences as provided by this input representation. Our system is tested both on writer dependent and writer independent tasks with vocabulary sizes ranging from 400 up to 20,000 words. For example, on a 20,000 word vocabulary we achieve word recognition rates up to 88.9% (writer dependent) and 84.1 % (writer independent) without using any language models.


Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks

Neural Information Processing Systems

On-line handwriting recognition is unusual among sequence labelling tasks in that the underlying generator of the observed data, i.e. the movement of the pen, is recorded directly. However, the raw data can be difficult to interpret because each letter is spread over many pen locations. As a consequence, sophisticated pre-processing is required to obtain inputs suitable for conventional sequence labelling algorithms, such as HMMs. In this paper we describe a system capable of directly transcribing raw on-line handwriting data. The system consists of a recurrent neural network trained for sequence labelling, combined with a probabilistic language model.


Motion-Based Handwriting Recognition

arXiv.org Artificial Intelligence

Sensor-Based Gesture Recognition Recently, there have It is prevalent in today's world for people to write on a been lots of researches for various ways of leveraging inertial touch screen with a smart pen, as there is a strong need to digitize motion unit (IMU) data to predict the gesture or the activity handwritten content, to make the review and indexing of users [7, 8, 9, 10, 11], but few studies make use of the IMU easier. However, despite the success of character recognition data to predict the handwriting letter due to the lack of relevant on digital devices [1, 2, 3], requiring a digitizer as the writing dataset. Oh et al. analyzed using inertial sensor based data to surface poses a possibly unnecessary restriction to overcome.


Recognition-based Segmentation of On-Line Cursive Handwriting

Neural Information Processing Systems

This paper introduces a new recognition-based segmentation approach torecognizing online cursive handwriting from a database of 10,000 English words. The original input stream of z, y pen coordinates isencoded as a sequence of uniform stroke descriptions that are processed by six feed-forward neural-networks, each designed to recognize letters of different sizes. Words are then recognized by performing best-first search over the space of all possible segmentations. Resultsdemonstrate that the method is effective at both writer dependent recognition (1.7% to 15.5% error rate) and writer independent recognition (5.2% to 31.1% error rate). 1 Introduction With the advent of pen-based computers, the problem of automatically recognizing handwriting from the motions of a pen has gained much significance. Progress has been made in reading disjoint block letters [Weissman et.