BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//scipy-2026//talk//3TBXB8
BEGIN:VTIMEZONE
TZID:CST
BEGIN:STANDARD
DTSTART:20001029T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10;UNTIL=20061029T080000Z
TZNAME:CST
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
END:STANDARD
BEGIN:STANDARD
DTSTART:20071104T030000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:CST
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000402T030000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4;UNTIL=20060402T090000Z
TZNAME:CDT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20070311T030000
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:CDT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-scipy-2026-3TBXB8@pretalx.com
DTSTART;TZID=CST:20260715T160500
DTEND;TZID=CST:20260715T163500
DESCRIPTION:Optical character recognition (OCR) has been a long standing me
 thod of extracting text data from images. Traditional OCR models rely on p
 attern recognition and feature extraction using computer vision techniques
  and specialized Python libraries. Recently\, large language models (LLMs)
  and generic AI assistants have provided an alternative method of text ext
 raction. This talk explores the efficacy of using LLMs and VLMs for inform
 ation extraction in production data pipelines and a data-driven approach f
 or evaluating them against traditional OCR methods in terms of accuracy\, 
 reliability\, latency\, and cost.
DTSTAMP:20260617T083110Z
LOCATION:Memorial Hall
SUMMARY:The future of OCR? Structured text extraction with LLMs - Patrick D
 eziel
URL:https://pretalx.com/scipy-2026/talk/3TBXB8/
END:VEVENT
END:VCALENDAR