diff --git a/src/textcode/gibberish.py b/src/textcode/gibberish.py
new file mode 100644
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+++ b/src/textcode/gibberish.py
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+#!/usr/bin/python
+#
+# From: https://raw.githubusercontent.com/yapus/gibberish/01637fe1fda827529ca76b8d6fee2de9100719f1/gibberish/gibberish.py
+#
+# 12Jun2017 Petr Janata - added srcfile and outfile
+# 17Jun2107 Petr Janata - expanded set of accepted characters to include digits and hyphen
+#
+# whch is based off of:
+# https://raw.githubusercontent.com/rrenaud/Gibberish-Detector/aa1d4e4555362b3dada97ebe6ecc23a84fc470fe/gib_detect_train.py
+#
+
+import math
+import pickle
+from pathlib import Path
+
+data_dir = Path(__file__).parent / 'data' / 'gibberish'
+model_path = data_dir / 'gib_model.pki'
+big_file_path = data_dir / 'big.txt'
+good_file_path = data_dir / 'good.txt'
+bad_file_path = data_dir / 'bad.txt'
+
+accepted_chars = 'abcdefghijklmnopqrstuvwxyz0123456789- '
+pos = dict([(char, idx) for idx, char in enumerate(accepted_chars)])
+
+
+class Gibberish(object):
+    def __init__(self):
+        if model_path.exists():
+            self.load_persisted_model()
+        else:
+            self.train()
+
+    def persist_model(self):
+        with open(model_path, mode='wb') as f:
+            pickle.dump(vars(self), f)
+
+    def load_persisted_model(self):
+        with open(model_path, mode='rb') as f:
+            persisted_model = pickle.load(f)
+            for key, value in persisted_model.items():
+                setattr(self, key, value)
+
+    def normalize(self, line):
+        """ Return only the subset of chars from accepted_chars.
+        This helps keep the  model relatively small by ignoring punctuation,
+        infrequenty symbols, etc. """
+        return [c.lower() for c in line if c.lower() in accepted_chars]
+
+    def ngram(self, n, l):
+        """ Return all n grams from l after normalizing """
+        filtered = self.normalize(l)
+        for start in range(0, len(filtered) - n + 1):
+            yield ''.join(filtered[start:start + n])
+
+    def avg_transition_prob(self, l, log_prob_mat):
+        """ Return the average transition prob from l through log_prob_mat. """
+        log_prob = 0.0
+        transition_ct = 0
+        for a, b in self.ngram(2, l):
+            log_prob += log_prob_mat[pos[a]][pos[b]]
+            transition_ct += 1
+        # The exponentiation translates from log probs to probs.
+        return math.exp(log_prob / (transition_ct or 1))
+
+    def train(self, bigfile=big_file_path, goodfile=good_file_path,
+              badfile=bad_file_path):
+        """ Write a simple model as a pickle file """
+        k = len(accepted_chars)
+        # Assume we have seen 10 of each character pair.  This acts as a kind of
+        # prior or smoothing factor.  This way, if we see a character transition
+        # live that we've never observed in the past, we won't assume the entire
+        # string has 0 probability.
+        counts = [[10 for i in range(k)] for i in range(k)]
+
+        # Count transitions from big text file, taken
+        # from http://norvig.com/spell-correct.html
+        for line in open(bigfile, encoding='utf-8'):
+            for a, b in self.ngram(2, line):
+                counts[pos[a]][pos[b]] += 1
+
+        # Normalize the counts so that they become log probabilities.
+        # We use log probabilities rather than straight probabilities to avoid
+        # numeric underflow issues with long texts.
+        # This contains a justification:
+        # http://squarecog.wordpress.com/2009/01/10/dealing-with-underflow-in-joint-probability-calculations/
+        for i, row in enumerate(counts):
+            s = float(sum(row))
+            for j in range(len(row)):
+                row[j] = math.log(row[j] / s)
+
+        # Find the probability of generating a few arbitrarily choosen good and
+        # bad phrases.
+        good_probs = [self.avg_transition_prob(l, counts) for l in open(goodfile, encoding='utf-8')]
+        bad_probs = [self.avg_transition_prob(l, counts) for l in open(badfile, encoding='utf-8')]
+
+        # Assert that we actually are capable of detecting the junk.
+        assert min(good_probs) > max(bad_probs)
+
+        # And pick a threshold halfway between the worst good and best bad inputs.
+        thresh = (min(good_probs) + max(bad_probs)) / 2
+        self.mat = counts
+        self.thresh = thresh
+        self.persist_model()
+
+    def detect_gibberish(self, text):
+        COPYRIGHT_INDICATORS = (
+            'copyright', '(c)', 'c)', '©', '@copyright', 
+            'author:', 'commit', 'portions:', 'rights reserved',
+            '(p)', 'trademark', 'intellectual property'
+        )
+        
+        text_lower = text.lower()
+        if any(indicator in text_lower for indicator in COPYRIGHT_INDICATORS):
+            return False
+        
+        text_normalized = ''.join(self.normalize(text))
+        return self.avg_transition_prob(text_normalized, self.mat) < self.thresh
+
+    def percent_gibberish(self, text):
+        text = ''.join(self.normalize(text))
+        text = text.strip()
+        words = text.split(' ')
+        if len(words) == 0:
+            return 0
+
+        gibberish_count = 0
+        for word in words:
+            if self.detect_gibberish(word):
+                gibberish_count += 1
+
+        return float(gibberish_count) / float(len(words))
+
+    def gibberish_pct(self, text):
+        text = ''.join(self.normalize(text))
+        return self.avg_transition_prob(text, self.mat)