!752

p {

  L<S> texts = splitAtSpace("a b c d");

  data = litorderedmap();

  for (S s : texts)

    data.put(renderTheText(s), s);

  push("Dummy");

  push("MD5Recognizer");

  solveImage2Text();

}

static class Dummy {

  O get(BWImage img) { ret "dummy"; }

}

static class MD5Recognizer {

  new Map<S, S> map;

  O get(BWImage img) { ret map.get(md5OfBWImage(img)); }

  void train(Map<BWImage, S> data) {

    for (BWImage img : keys(data)) {

      S text = data.get(img);

      map.put(md5OfBWImage(img), text);

    }

  }

}

static Font renderTheText_font;

static BWImage renderTheText(S s) {

  if (renderTheText_font == null)

    renderTheText_font = loadFont("#1004569"); // "Ticketing"

  ret new BWImage(renderText(renderTheText_font, 20f, s));

}

static void push(S solver) {

  newProducts.add(solver);

}

// solvers are func(BWImage) -> S

static Map<BWImage, S> data;

static new LinkedBlockingQueue<S> newProducts;

static new Best best;

sclass Best {

  S solver;

  double score;

  void update(S solver, double score) {

    if (score > this.score) {

      print("New best score: " + score);

      this.score = score;

      this.solver = solver;

    }

  }

}

static void solveImage2Text() {

  while licensed {

    testSolver(grabFromQueue(newProducts));

  }

}

static void testSolver(S solver) {

  O realSolver = unstructure(solver);

  train(realSolver, data);

  double score = score(realSolver, data);

  print(formatDouble(score, 1) + ": " + solver);

  best.update(solver, score);

}

static void train(O realSolver, Map<BWImage, S> data) {

  callOpt(realSolver, "train", data);

}

static double score(O realSolver, Map<BWImage, S> data) {

  double score = 0;

  int n = 0;

  for (BWImage img : keys(data)) {

    S text = data.get(img);

    ++n;

    try {

      if (eq(callF(realSolver, img), text))

        ++score;

    } catch e {

      silentException(e);

    }

  }

  ret score*100/n;

}