nn.cfg

An open source handwriting recongnition package!!!

# CommonPreProc::dehookTraces;
# CommonPreProc::normalizeOrientation;
# CommonPreProc::resampleTraceGroup;
# Default value: {CommonPreProc::normalizeSize,CommonPreProc::resampleTraceGroup,CommonPreProc::normalizeSize}
#-------------------------------------------------------------------------------
NNPreprocSequence={CommonPreProc::normalizeSize,CommonPreProc::resampleTraceGroup,CommonPreProc::normalizeSize}

#---------------------------------------
#	TRAINING
#---------------------------------------

#-------------------------------------------------------------------------------
# NNTrainPrototypeSelectionMethod
# 
# Description: This is used to specify the prototype selection method to be used 
# while training the shape recognizer. When set to hier-clustering, the 
# prototypes are selected using hierarchical clustering method.
#
# Valid value: [hier-clustering]
# Default value: hier-clustering
#-------------------------------------------------------------------------------
NNTrainPrototypeSelectionMethod=hier-clustering


#-------------------------------------------------------------------------------
# NNTrainPrototypeReductionFactorPerClass
#
# Description: This config parameter is used only when the prototype selection 
# is clustering. This config parameter is used to specify the amount of the 
# initial prototypes to be excluded during prototype selection.  
# Set it to automatic if the number of clusters is to be determined 
# automatically. Set it to none if no prototype selection is required. If the 
# value of this parameter is set to a number between 1-100, say 25, then 75% 
# (i.e 100-25) of the initial training data are retained as prototypes. 
# This parameter can be specified only if the NNTrainNumPrototypesPerClass
# is not specified.
#
# Valid value: [automatic | none | any real number from 0-100] 
# Default value: automatic
#-------------------------------------------------------------------------------
NNTrainPrototypeReductionFactorPerClass = none

#-------------------------------------------------------------------------------
# NNTrainNumPrototypesPerClass
#
# Description: This config parameter is used only when the prototype selection 
# is clustering. This is used to specify the number of prototypes to be selected 
# from the training data. This parameter can be specified only if 
# PrototypeReductionFactor is not specified. This config entry is commented as 
# only one of NNTrainPrototypeReductionFactorPerClass or 
# NNTrainNumPrototypesPerClass can be active in a valid cfg file.
#
# Valid value: [automatic | none | any integer from 1-N]
#              (N is the number of samples # per class)
# Default value: automatic
#-------------------------------------------------------------------------------  
#NNTrainNumPrototypesPerClass=automatic

# Note: Only one of either PrototypeReductionFactor or NumClusters can be 
# enabled at any particular instance 

#-----------------------------------------
#	FEATURE EXTRACTION
#-----------------------------------------

#-------------------------------------------------------------------------------
# FeatureExtractor
#
# Description: The configuration value is used to specify the feature extraction 
# module to be used for feature extraction. The point float feature extraction 
# module extracts the x,y,cosine and sine angle features at every point of the 
# character.
#
# Valid value: [PointFloatShapeFeatureExtractor]
# Default value: PointFloatShapeFeatureExtractor
#-------------------------------------------------------------------------------
FeatureExtractor=PointFloatShapeFeatureExtractor

#-----------------------------------------
#	RECOGNITION
#-----------------------------------------

#-------------------------------------------------------------------------------
# NNRecoDTWEuFilterOutputSize
# 
# Description: This config parameter is used to set the number of nearest 
# neighbours (filtered based on euclidean distance)to be considered for 
# calculating dtw distance. Set to all if all samples are to be considered for
# calculating dtw distance. This is mainly used to increase the speed of 
# recognition.
#
# Valid value: [all| any integer from 1-N](N is the size of prototype set)  
# Default Value: all
#-------------------------------------------------------------------------------
NNRecoDTWEuFilterOutputSize = all

#-------------------------------------------------------------------------------
# NNRecoRejectThreshold
# 
# Description: Threshold to reject the test sample. If the confidence obtained 
# for the recognition of test sample is less than this threshold then the test 
# sample is rejected. 
#
# Valid value: Any real number from 0-1
# Default value: 0.001
#-------------------------------------------------------------------------------
NNRecoRejectThreshold = 0.001

#-------------------------------------------------------------------------------
# NNRecoNumNearestNeighbors
# 
# Description: Number of nearest neighbors to be considered during recognition
# and computation of confidence. If the value is set to 1, nearest neighbor 
# classifier is used, otherwise k-nearest neighbor or Adaptive k-nearest 
# neighbor classifiers are used. By default, nearest neighbor classifier is used.
#
# Valid value: Any integer >= 1
# Default value: 1
#-------------------------------------------------------------------------------
NNRecoNumNearestNeighbors = 1

#-------------------------------------------------------------------------------
# NNRecoUseAdaptiveKNN
#
# Description: This parameter is used to specify whether Adaptive k-nearest 
# neighbor recognizer (A-kNN) is to be used. If set to true, A-kNN recognizer is 
# used, otherwise kNN recognizer is used.  The A-kNN recognizer automatically 
# determines the number of nearest neighbors to be considered for recognition in 
# each class. If NNRecoNumNearestNeighbors is set to 1, this parameter is 
# automatically set to false and the manually set value will not be considered.
#
# Valid value: [true | false]
# Default value: false
#-------------------------------------------------------------------------------
NNRecoUseAdaptiveKNN = false

#--------------------------------------------
#      COMMON FOR TRAINING AND RECOGNITION
#--------------------------------------------

#-------------------------------------------------------------------------------
# NNPrototypeDistanceMeasure
#
# Description: This configuration parameter is used to specify the distance 
# measure to be used in clustering and recognition. DTW or Euclidean distance 
# measures can be used.
# 
# Valid value [dtw | eu]
# Default value: dtw
#-------------------------------------------------------------------------------
NNPrototypeDistanceMeasure = dtw

#-------------------------------------------------------------------------------
# NNDTWBandingRadius
#
# Description: This configuration parameter specifies the banding radius
# to be used for DTW computation. This is used to speed up the computation
# process. If this value is zero no banding is done. The value is specified as
# fraction of ResampTraceDimension to be used while computing the DTW
# distance.
#
# Valid values: Any real number > 0 and <= 1
# Default Value: 0.33
#-------------------------------------------------------------------------------
NNDTWBandingRadius=0.33

#-------------------------------------------------------------------------------
# NNDTWBandingRadius
#
# Description: This configuration parameter specifies the mode for 
# opening the mdt file. 
#
# Valid values: ascii, binary
# Default Value: ascii
#-------------------------------------------------------------------------------

NNMDTFileOpenMode=ascii