Module: KvgCharacterRecognition::Recognizer

Extended by:
Trainer
Defined in:
lib/kvg_character_recognition/recognizer.rb

Overview

This class contains methods calculating similarity scores between input pattern and template patterns

Class Method Summary collapse

Methods included from Trainer

heatmaps, preprocess

Class Method Details

.scores(strokes, datastore) ⇒ Object

This method calculates similarity scores which is an average of the somehow weighted sum of the euclidean distance of

  1. 17x17 smoothed heatmap

  2. manhattan distance of directional feature densities in average

Params:

strokes

strokes are not preprocessed

datastore

JSONDatastore or custom datastore type having method characters_in_stroke_range(min..max)



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# File 'lib/kvg_character_recognition/recognizer.rb', line 22

def self.scores strokes, datastore
  strokes = preprocess(strokes)
  heatmaps = heatmaps(strokes)
  templates = select_templates datastore, @number_of_points, strokes.count

  #scores = datastore.data.map do |cand|
  scores = templates.map do |cand|
    score = Math.manhattan_distance(heatmaps[0], cand[:heatmaps][0]) +
            Math.manhattan_distance(heatmaps[1], cand[:heatmaps][1]) +
            Math.manhattan_distance(heatmaps[2], cand[:heatmaps][2])
    [score, cand]
  end

  scores.sort{ |a, b| a[0] <=> b[0] }
end

.select_templates(datastore, number_of_points, number_of_strokes) ⇒ Object

This method selects all templates from the database which should be further examined It filtered out those characters with a too great difference in number of points and strokes to the input character



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# File 'lib/kvg_character_recognition/recognizer.rb', line 8

def self.select_templates datastore, number_of_points, number_of_strokes
  p_min = number_of_points - 100
  p_max = number_of_points + 100
  s_min = number_of_strokes - 12
  s_max = number_of_strokes + 12
  datastore.characters_in_range(p_min..p_max, s_min..s_max)
end