Topology inference for an ANN/HMM hybrid on-line handwriting recognition system

2002 ◽  
Author(s):  
Haifeng Li ◽  
T. Artieres ◽  
P. Gallinari ◽  
B. Dorizzi
Keyword(s):  
Handwriting Recognition ◽  
Recognition System ◽  
Topology Inference ◽  
On Line

scholarly journals A Comprehensive Survey on On-line Handwriting Recognition Technology and its Real Application to the Nepalese Natural Handwriting

1970 ◽  
Vol 5 (1) ◽  
pp. 31-55 ◽  
Author(s):  
Santosh KC ◽  
Cholwich Nattee
Keyword(s):  
Dynamic Time Warping ◽  
Feature Vector ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Time Warping ◽  
Real Application ◽  
Non Linear ◽  
Comprehensive Survey ◽  
On Line ◽  
Dynamic Time

Handwriting Recognition Technology has been improving much under the purview of pattern recognition and image processing since a few decades. This paper focuses on the comprehensive survey on on-line handwriting recognition system along with the real application by taking Nepali natural handwriting (a real example of one of the cursive handwritings). The survey mainly includes pre-processing, feature vector and similarity measures in between the non-linear 2D sequences of coordinates, and their effective applications. A very highlighting topic "Dynamic Time Warping Algorithm'' (DTW) is introduced, which has been popular in determining the distance between two non-linear sequences ranging from handwriting to speech recognition. Besides these contemporary research issues/areas, stroke number and order free Nepalese natural handwritten recognition system is presented in the second step. Writing one's own style brings unevenness in writing units, which is the most difficult part to classify. Writing units reveal number, shape, size, order of stroke, and speed in writing. Variation in the number of strokes, their order, shapes and sizes, tilting angles and similarities among characters from one another are the important factors, which are to be considered in classification for Nepali. This paper utilizes structural properties of those alphanumeric characters, which have variable writing units. It uses a string of pen tip's positions and tangent angles of every consecutive point as a feature vector sequence of a stroke. We constructed a prototype recognizer that uses the DTW algorithm to align handwritten strokes with stored strokes' templates and determine their similarity. Separate system is trained for original and preprocessed writing samples and achieved recognition rates of 85.87% and 88.59% respectively. This introduces novel real time handwriting recognition on Nepalese alphanumeric characters, which are independent of number of strokes, as well as their order. Key Words: Handwriting Recognition System; Pre-processing; Feature Vector; Dynamic Time Warping; Agglomerating Hierarchical Clustering; Nepali. DOI: 10.3126/kuset.v5i1.2845 Kathmandu University Journal of Science, Engineering and Technology Vol.5, No.1, January 2009, pp 31-55

On-Line Handwriting Recognition Based on Hopfield Neural Network

2014 ◽  
Vol 610 ◽  
pp. 265-269
Author(s):  
Jing Ya Zhang ◽  
Li Yang ◽  
Rong Zhao ◽  
Long Hua Yang
Keyword(s):  
Neural Network ◽  
Disturbance Rejection ◽  
Recognition Performance ◽  
Handwriting Recognition ◽  
Hopfield Neural Network ◽  
Recognition System ◽  
Performance Simulation ◽  
On Line ◽  
Neural Network Toolbox ◽  
Simulation Results

In this paper, Discrete Hopfield Neural Network (DHNN) is adopted to realize handwritten characters recognition. First, learning samples are preprocessed including binarization, normalization and interpolation. Then pixel features are extracted and used to establish DHNN. The handwritten test samples and noise corrupted samples are finally inputted into the network to verify its recognition performance. Simulation results reveal that DHNN has good fault tolerance and disturbance rejection performance. In addition, the recognition system is realized with MATLAB neural network toolbox and GUI, which verifies the feasibility of the algorithm.

AN ON-LINE HANDWRITING-RECOGNITION SYSTEM BASED ON UNRELIABLE MODULES

1990 ◽  
pp. 167-185 ◽  
Author(s):  
HANS-LEO TEULINGS ◽  
LAMBERT R.B. SCHOMAKER ◽  
JAN GERRITSEN ◽  
HANS DREXLER ◽  
MARC ALBERS
Keyword(s):  
Handwriting Recognition ◽  
Recognition System ◽  
On Line
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