  This work explores how to fine-tune large language models using prompt
engineering techniques with contextual information for generating an accurate
text description of the full story, ready to be forwarded to off-the-shelve
speech synthesis tools. We propose to use existing computer vision and optical
character recognition techniques to build a grounded context from the comic
strip image content, such as panels, characters, text, reading order and the
association of bubbles and characters. Then we infer character identification
and generate comic book script with context-aware panel description including
character's appearance, posture, mood, dialogues etc. We believe that such
enriched content description can be easily used to produce audiobook and eBook
with various voices for characters, captions and playing sound effects.
