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Recognition-based Segmentation of On-Line Cursive Handwriting

Neural Information Processing Systems

This paper introduces a new recognition-based segmentation approach to recognizing online cursive handwriting from a database of 10,000 English words. The original input stream of z, y pen coordinates is encoded 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. Results demonstrate 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.


Recognition-based Segmentation of On-Line Cursive Handwriting

Neural Information Processing Systems

This paper introduces a new recognition-based segmentation approach to recognizing online cursive handwriting from a database of 10,000 English words. The original input stream of z, y pen coordinates is encoded 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. Results demonstrate 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.


Recognition-based Segmentation of On-Line Hand-printed Words

Neural Information Processing Systems

The input strings consist of a timeordered sequence of XY coordinates, punctuated by pen-lifts. The methods were designed to work in "run-on mode" where there is no constraint on the spacing between characters. While both methods use a neural network recognition engine and a graph-algorithmic post-processor, their approaches to segmentation are quite different. The first method, which we call IN SEC (for input segmentation), uses a combination of heuristics to identify particular penlifts as tentative segmentation points. The second method, which we call OUTSEC (for output segmentation), relies on the empirically trained recognition engine for both recognizing characters and identifying relevant segmentation points.


Recognition-based Segmentation of On-Line Hand-printed Words

Neural Information Processing Systems

The input strings consist of a timeordered sequence of XY coordinates, punctuated by pen-lifts. The methods were designed to work in "run-on mode" where there is no constraint on the spacing between characters. While both methods use a neural network recognition engine and a graph-algorithmic post-processor, their approaches to segmentation are quite different. The first method, which we call IN SEC (for input segmentation), uses a combination of heuristics to identify particular penlifts as tentative segmentation points. The second method, which we call OUTSEC (for output segmentation), relies on the empirically trained recognition engine for both recognizing characters and identifying relevant segmentation points.


Recognition-based Segmentation of On-Line Hand-printed Words

Neural Information Processing Systems

The input strings consist of a timeordered sequenceof XY coordinates, punctuated by pen-lifts. The methods were designed to work in "run-on mode" where there is no constraint on the spacing between characters. While both methods use a neural network recognition engine and a graph-algorithmic post-processor, their approaches to segmentation are quite different. Thefirst method, which we call IN SEC (for input segmentation), usesa combination of heuristics to identify particular penlifts as tentative segmentation points. The second method, which we call OUTSEC (for output segmentation), relies on the empirically trainedrecognition engine for both recognizing characters and identifying relevant segmentation points. 1 INTRODUCTION We address the problem of writer independent recognition of hand-printed words from an 80,OOO-word English dictionary. Several levels of difficulty in the recognition of hand-printed words are illustrated in figure 1. The examples were extracted from our databases (table 1). Except in the cases of boxed or clearly spaced characters, segmenting characters independently of the recognition process yields poor recognition performance.This has motivated us to explore recognition-based segmentation techniques.