diff --git a/src/calibre/ebooks/conversion/preprocess.py b/src/calibre/ebooks/conversion/preprocess.py
index d2bdba4928..54639df93c 100644
--- a/src/calibre/ebooks/conversion/preprocess.py
+++ b/src/calibre/ebooks/conversion/preprocess.py
@@ -473,12 +473,6 @@ class HTMLPreProcessor(object):
             # unwrap/delete soft hyphens with formatting
             end_rules.append((re.compile(u'[­]\s*(</(i|u|b)>)+(</p>\s*<p>\s*)+\s*(<(i|u|b)>)+\s*(?=[[a-z\d])'), lambda match: ''))
 
-        # Make the more aggressive chapter marking regex optional with the preprocess option to
-        # reduce false positives and move after header/footer removal
-        if getattr(self.extra_opts, 'preprocess_html', None):
-            if is_pdftohtml:
-                end_rules.append((re.compile(r'<p>\s*(?P<chap>(<[ibu]>){0,2}\s*([A-Z \'"!]{3,})\s*([\dA-Z:]+\s){0,4}\s*(</[ibu]>){0,2})\s*<p>\s*(?P<title>(<[ibu]>){0,2}(\s*\w+){1,4}\s*(</[ibu]>){0,2}\s*<p>)?'), chap_head),)
-
         length = -1
         if getattr(self.extra_opts, 'unwrap_factor', 0.0) > 0.01:
             docanalysis = DocAnalysis('pdf', html)
@@ -525,11 +519,10 @@ class HTMLPreProcessor(object):
             html = dehyphenator(html,'html', length)
 
         if is_pdftohtml:
-            from calibre.ebooks.conversion.utils import PreProcessor
-            pdf_markup = PreProcessor(self.extra_opts, None)
+            from calibre.ebooks.conversion.utils import HeuristicProcessor
+            pdf_markup = HeuristicProcessor(self.extra_opts, None)
             totalwords = 0
-            totalwords = pdf_markup.get_word_count(html)
-            if totalwords > 7000:
+            if pdf_markup.get_word_count(html) > 7000:
                 html = pdf_markup.markup_chapters(html, totalwords, True)
 
         #dump(html, 'post-preprocess')
diff --git a/src/calibre/ebooks/conversion/utils.py b/src/calibre/ebooks/conversion/utils.py
index 1a691b2e14..888d24d791 100644
--- a/src/calibre/ebooks/conversion/utils.py
+++ b/src/calibre/ebooks/conversion/utils.py
@@ -170,9 +170,9 @@ class HeuristicProcessor(object):
         # minimum of chapters to search for.  A max limit is calculated to prevent things like OCR
         # or pdf page numbers from being treated as TOC markers
         if wordcount > 7000:
-            self.min_chapters = int(ceil(wordcount / 7000.))
-            self.max_chapters = int(ceil(wordcount / 100.))
-        #print "minimum chapters required are: "+str(self.min_chapters)
+            self.min_chapters = int(ceil(wordcount / 15000.))
+            self.max_chapters = int(ceil(wordcount / 1200.))
+        print "minimum chapters required are: "+str(self.min_chapters)
         heading = re.compile('<h[1-3][^>]*>', re.IGNORECASE)
         self.html_preprocess_sections = len(heading.findall(html))
         self.log.debug("found " + unicode(self.html_preprocess_sections) + " pre-existing headings")
@@ -272,6 +272,7 @@ class HeuristicProcessor(object):
                             title_req = True
                             strict_title = False
                         self.log.debug(unicode(type_name)+" had "+unicode(hits)+" hits - "+unicode(self.chapters_no_title)+" chapters with no title, "+unicode(self.chapters_with_title)+" chapters with titles, "+unicode(float(self.chapters_with_title) / float(hits))+" percent. ")
+                        print "max chapters is "+str(self.max_chapters)
                         if type_name == 'common':
                             analysis_result.append([chapter_type, n_lookahead_req, strict_title, ignorecase, title_req, log_message, type_name])
                         elif self.min_chapters <= hits < self.max_chapters: