  We introduce new techniques for extracting, analyzing, and visualizing
textual contents from instructional videos of low production quality. Using
Automatic Speech Recognition, approximate transcripts (H75% Word Error Rate)
are obtained from the originally highly compressed videos of university
courses, each comprising between 10 to 30 lectures. Text material in the form
of books or papers that accompany the course are then used to filter meaningful
phrases from the seemingly incoherent transcripts. The resulting index into the
transcripts is tied together and visualized in 3 experimental graphs that help
in understanding the overall course structure and provide a tool for localizing
certain topics for indexing. We specifically discuss a Transcript Index Map,
which graphically lays out key phrases for a course, a Textbook Chapter to
Transcript Match, and finally a Lecture Transcript Similarity graph, which
clusters semantically similar lectures. We test our methods and tools on 7 full
courses with 230 hours of video and 273 transcripts. We are able to extract up
to 98 unique key terms for a given transcript and up to 347 unique key terms
for an entire course. The accuracy of the Textbook Chapter to Transcript Match
exceeds 70% on average. The methods used can be applied to genres of video in
which there are recurrent thematic words (news, sports, meetings,...)
