DM1901

CASE STUDY: NEW YORK TIMES Dm
The Times to protect one of their most
unique assets migrating from steel filing
cabinets to a cloud-based platform where
journalists can bring visual storytelling to a
whole new level."
Simply storing high-resolution images is
not enough to create a system that photo
editors can easily use. A working asset
management system must allow the users
to be able to browse and search for photos
easily. The Times built a processing pipeline
that stores and processes the photos and
will use cloud technology to process and
recognise text, handwriting and other
details that can be found in the images.
MACHINE LEARNING ADDS INSIGHTS
Storing the images is only one half of
the story. To make an archive like The
Times' morgue even more accessible and
useful, it's beneficial to leverage
additional GCP features. In the case of
The Times, one of the bigger challenges
in scanning their photo archive has been
adding data regarding the contents of
the images. Google's Cloud Vision API
helps fill that gap.
The photo (above) of the old Penn
Station from The Times gives an example.
Without additional context, it's not clear
from the front of the photo what it
contains, but the back of the photo
contains a wealth of useful information,
and the Cloud Vision API can help
process, store, and read it. When we
submit the back of the image to the API
with no additional processing, the Cloud
Vision API detects the text and recognises
data such as dates, descriptions ('The
scene in Pennsylvania Station yesterday
afternoon') and context ('Pub NYT Sun
5/2/93', 'RECEIVED DEC 25 1942'). Of
course, the digital text transcription isn't
perfect, but it's faster and more cost
effective than alternatives for processing
millions of images.
This is only the start of what's possible for
companies with physical archives. They can
use the Vision API to identify objects,
places and images. For example, if we pass
the black and white photo above through
the Cloud Vision API with Logo Detection,
it recognises Pennsylvania Station.
Furthermore, AutoML can be used to
better identify images in collections using a
corpus of already captioned images.
The Cloud Natural Language API could
also be used to add additional semantic
information to recognised text. For
example, passing the text "THE WAY IT
WAS - Crowded Penn Station in 1942, an
era when only the brave flew - to
Washington, Miami and assorted way
stations." through the Cloud Natural
Language API, it correctly identifies "Penn
Station," "Washington," and "Miami" as
locations, and classifies the entire sentence
into the category "travel" and the
subcategory "bus & rail."
Going forward the NYT newsroom will
use the digitised archives to inspire stories
for Past Tense, a body of coverage
dedicated to revisiting history. The first
package from The Times newsroom to
utilise the digitised archives will focus on
how The Times covered California in the
20th century, examining how California's
free-spiritedness and culture of recreation
and innovation appeared to Times
journalists 3,000 miles away.
More info: cloud.google.com
www.document-manager.com
January/February 2019
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