Machine Generated Data
Tags
Amazon
created on 2022-06-04
Railway | 99.8 | |
| ||
Rail | 99.8 | |
| ||
Train Track | 99.8 | |
| ||
Transportation | 99.8 | |
| ||
Vehicle | 81 | |
| ||
Architecture | 76.1 | |
| ||
Building | 76.1 | |
| ||
Train | 72.6 | |
| ||
Metropolis | 71 | |
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Urban | 71 | |
| ||
City | 71 | |
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Town | 71 | |
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Spire | 70.5 | |
| ||
Tower | 70.5 | |
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Steeple | 70.5 | |
| ||
Terminal | 65.3 | |
| ||
Train Station | 59.9 | |
|
Imagga
created on 2022-06-04
liner | 47.7 | |
| ||
city | 39.7 | |
| ||
passenger ship | 38.5 | |
| ||
ship | 37.6 | |
| ||
building | 33.3 | |
| ||
architecture | 33.2 | |
| ||
bridge | 32.8 | |
| ||
vessel | 29.2 | |
| ||
tower | 28.6 | |
| ||
urban | 28 | |
| ||
sky | 24.9 | |
| ||
night | 24.9 | |
| ||
travel | 24.6 | |
| ||
transportation | 23.3 | |
| ||
river | 23.1 | |
| ||
deck | 21.6 | |
| ||
structure | 20.6 | |
| ||
cityscape | 18.9 | |
| ||
transport | 18.3 | |
| ||
landmark | 18 | |
| ||
town | 17.6 | |
| ||
street | 17.5 | |
| ||
road | 17.2 | |
| ||
construction | 17.1 | |
| ||
traffic | 17.1 | |
| ||
water | 16.7 | |
| ||
steel | 15.9 | |
| ||
skyline | 15.2 | |
| ||
famous | 14.9 | |
| ||
scene | 14.7 | |
| ||
highway | 13.5 | |
| ||
tourism | 13.2 | |
| ||
park | 13.1 | |
| ||
vehicle | 13 | |
| ||
industry | 12.8 | |
| ||
industrial | 12.7 | |
| ||
modern | 12.6 | |
| ||
port | 12.5 | |
| ||
harbor | 12.5 | |
| ||
evening | 12.1 | |
| ||
old | 11.8 | |
| ||
craft | 11.7 | |
| ||
exterior | 11.1 | |
| ||
station | 11 | |
| ||
boats | 10.7 | |
| ||
marina | 10.5 | |
| ||
landscape | 10.4 | |
| ||
house | 10.4 | |
| ||
boat | 10.2 | |
| ||
church | 10.2 | |
| ||
sea | 10.2 | |
| ||
speed | 10.1 | |
| ||
tourist | 10 | |
| ||
skyscraper | 10 | |
| ||
capital | 9.8 | |
| ||
history | 9.8 | |
| ||
high | 9.5 | |
| ||
dusk | 9.5 | |
| ||
motion | 9.4 | |
| ||
car | 9.3 | |
| ||
window | 9.2 | |
| ||
business | 9.1 | |
| ||
train | 8.8 | |
| ||
twilight | 8.7 | |
| ||
aerial | 8.7 | |
| ||
light | 8.7 | |
| ||
downtown | 8.6 | |
| ||
roof | 8.6 | |
| ||
panorama | 8.6 | |
| ||
reflection | 8.4 | |
| ||
monument | 8.4 | |
| ||
iron | 8.4 | |
| ||
pier | 8.2 | |
| ||
metal | 8 | |
| ||
line | 8 | |
| ||
sunny | 7.7 | |
| ||
engineering | 7.6 | |
| ||
track | 7.6 | |
| ||
buildings | 7.6 | |
| ||
technology | 7.4 | |
| ||
lights | 7.4 | |
| ||
waterfront | 7.1 | |
| ||
tract | 7 | |
|
Google
created on 2022-06-04
Black | 89.6 | |
| ||
Building | 89.5 | |
| ||
Black-and-white | 83 | |
| ||
Line | 82 | |
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Electricity | 81 | |
| ||
Parallel | 77.6 | |
| ||
Track | 77.3 | |
| ||
Rectangle | 76.4 | |
| ||
Composite material | 75.8 | |
| ||
City | 75.3 | |
| ||
Urban design | 74.7 | |
| ||
Railway | 72.8 | |
| ||
Monochrome | 72.7 | |
| ||
Monochrome photography | 71 | |
| ||
Engineering | 70.8 | |
| ||
Metal | 70.3 | |
| ||
Facade | 68.8 | |
| ||
Symmetry | 67.6 | |
| ||
Steel | 63.5 | |
| ||
Nonbuilding structure | 63.3 | |
|
Color Analysis
Categories
Imagga
cars vehicles | 99.4% | |
|
Captions
Microsoft
created on 2022-06-04
a train on a steel track | 65.7% | |
| ||
a train on a train track with buildings in the background | 65.6% | |
| ||
a train on a track | 63.9% | |
|
Text analysis
Amazon
![](https://ids.lib.harvard.edu/ids/iiif/20489193/624,283,122,43/full/0/native.jpg)
MILL
![](https://ids.lib.harvard.edu/ids/iiif/20489193/500,283,245,48/full/0/native.jpg)
GRIST MILL
![](https://ids.lib.harvard.edu/ids/iiif/20489193/501,290,125,41/full/0/native.jpg)
GRIST
![](https://ids.lib.harvard.edu/ids/iiif/20489193/1,371,39,16/full/0/native.jpg)
PENDABLE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/500,341,193,43/full/0/native.jpg)
COFFEE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/48,411,30,11/full/0/native.jpg)
ACCOUNT
![](https://ids.lib.harvard.edu/ids/iiif/20489193/632,387,13,12/full/0/native.jpg)
IT
![](https://ids.lib.harvard.edu/ids/iiif/20489193/17,409,28,12/full/0/native.jpg)
CHARGE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/521,333,47,12/full/0/native.jpg)
BEST
![](https://ids.lib.harvard.edu/ids/iiif/20489193/649,385,44,16/full/0/native.jpg)
TASTE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/680,328,30,11/full/0/native.jpg)
FOR
![](https://ids.lib.harvard.edu/ids/iiif/20489193/521,326,190,20/full/0/native.jpg)
BEST SUBSTITUTE FOR
![](https://ids.lib.harvard.edu/ids/iiif/20489193/560,403,46,11/full/0/native.jpg)
NERVES
![](https://ids.lib.harvard.edu/ids/iiif/20489193/546,388,53,13/full/0/native.jpg)
BEVERAGE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/571,327,105,17/full/0/native.jpg)
SUBSTITUTE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/272,403,17,14/full/0/native.jpg)
FO
![](https://ids.lib.harvard.edu/ids/iiif/20489193/507,406,24,8/full/0/native.jpg)
OMA
![](https://ids.lib.harvard.edu/ids/iiif/20489193/707,386,15,11/full/0/native.jpg)
G
![](https://ids.lib.harvard.edu/ids/iiif/20489193/276,359,11,8/full/0/native.jpg)
PA
![](https://ids.lib.harvard.edu/ids/iiif/20489193/1,408,77,13/full/0/native.jpg)
EN A CHARGE ACCOUNT
![](https://ids.lib.harvard.edu/ids/iiif/20489193/219,457,17,15/full/0/native.jpg)
M
![](https://ids.lib.harvard.edu/ids/iiif/20489193/34,397,8,7/full/0/native.jpg)
K
![](https://ids.lib.harvard.edu/ids/iiif/20489193/632,385,113,16/full/0/native.jpg)
IT TASTE G OC
![](https://ids.lib.harvard.edu/ids/iiif/20489193/401,408,18,9/full/0/native.jpg)
DES
![](https://ids.lib.harvard.edu/ids/iiif/20489193/1,408,16,11/full/0/native.jpg)
EN A
![](https://ids.lib.harvard.edu/ids/iiif/20489193/504,388,95,14/full/0/native.jpg)
LIABLE BEVERAGE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/423,409,13,6/full/0/native.jpg)
NO
![](https://ids.lib.harvard.edu/ids/iiif/20489193/507,403,99,11/full/0/native.jpg)
OMA CH .. NERVES
![](https://ids.lib.harvard.edu/ids/iiif/20489193/319,454,19,15/full/0/native.jpg)
GUM
![](https://ids.lib.harvard.edu/ids/iiif/20489193/442,393,19,12/full/0/native.jpg)
AL
![](https://ids.lib.harvard.edu/ids/iiif/20489193/0,371,78,19/full/0/native.jpg)
PENDABLE CLETHING
![](https://ids.lib.harvard.edu/ids/iiif/20489193/401,407,63,11/full/0/native.jpg)
DES NO UNO
![](https://ids.lib.harvard.edu/ids/iiif/20489193/937,783,15,16/full/0/native.jpg)
B
![](https://ids.lib.harvard.edu/ids/iiif/20489193/532,406,14,7/full/0/native.jpg)
CH
![](https://ids.lib.harvard.edu/ids/iiif/20489193/1,394,41,9/full/0/native.jpg)
as MONEK K
![](https://ids.lib.harvard.edu/ids/iiif/20489193/58,397,15,7/full/0/native.jpg)
HELL
![](https://ids.lib.harvard.edu/ids/iiif/20489193/294,444,18,15/full/0/native.jpg)
EEM
![](https://ids.lib.harvard.edu/ids/iiif/20489193/504,390,40,11/full/0/native.jpg)
LIABLE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/401,441,15,11/full/0/native.jpg)
OXIE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/196,687,33,23/full/0/native.jpg)
12%
![](https://ids.lib.harvard.edu/ids/iiif/20489193/45,397,28,7/full/0/native.jpg)
im HELL
![](https://ids.lib.harvard.edu/ids/iiif/20489193/727,385,18,13/full/0/native.jpg)
OC
![](https://ids.lib.harvard.edu/ids/iiif/20489193/549,408,7,4/full/0/native.jpg)
..
![](https://ids.lib.harvard.edu/ids/iiif/20489193/13,394,21,8/full/0/native.jpg)
MONEK
![](https://ids.lib.harvard.edu/ids/iiif/20489193/1,394,11,7/full/0/native.jpg)
as
![](https://ids.lib.harvard.edu/ids/iiif/20489193/45,397,11,7/full/0/native.jpg)
im
![](https://ids.lib.harvard.edu/ids/iiif/20489193/43,374,34,16/full/0/native.jpg)
CLETHING
![](https://ids.lib.harvard.edu/ids/iiif/20489193/444,407,19,8/full/0/native.jpg)
UNO
![](https://ids.lib.harvard.edu/ids/iiif/20489193/752,338,34,22/full/0/native.jpg)
STOREGO
![](https://ids.lib.harvard.edu/ids/iiif/20489193/296,458,16,10/full/0/native.jpg)
REI
![](https://ids.lib.harvard.edu/ids/iiif/20489193/30,732,49,32/full/0/native.jpg)
bit
![](https://ids.lib.harvard.edu/ids/iiif/20489193/901,779,32,20/full/0/native.jpg)
99
![](https://ids.lib.harvard.edu/ids/iiif/20489193/365,441,21,13/full/0/native.jpg)
Monica
![](https://ids.lib.harvard.edu/ids/iiif/20489193/365,460,16,9/full/0/native.jpg)
Suse
![](https://ids.lib.harvard.edu/ids/iiif/20489193/113,710,42,26/full/0/native.jpg)
000
![](https://ids.lib.harvard.edu/ids/iiif/20489193/1,2,952,805/full/0/native.jpg)
PENDABLE CLE
15 GIRLS
EN A CHARGE ACCOUNT
o
xxx 2.9
GRIST MILL
E BEST SUBSTITUTE FOR
COFFEE
L1 BLE BEVERAGE
OMACH NERVES
IT TASTE GOOD
1.
9918
![](https://ids.lib.harvard.edu/ids/iiif/20489193/2,372,44,19/full/0/native.jpg)
PENDABLE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/44,374,21,18/full/0/native.jpg)
CLE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/43,398,10,12/full/0/native.jpg)
15
![](https://ids.lib.harvard.edu/ids/iiif/20489193/59,398,20,12/full/0/native.jpg)
GIRLS
![](https://ids.lib.harvard.edu/ids/iiif/20489193/3,410,13,14/full/0/native.jpg)
EN
![](https://ids.lib.harvard.edu/ids/iiif/20489193/13,410,9,14/full/0/native.jpg)
A
![](https://ids.lib.harvard.edu/ids/iiif/20489193/20,410,31,16/full/0/native.jpg)
CHARGE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/49,411,34,16/full/0/native.jpg)
ACCOUNT
![](https://ids.lib.harvard.edu/ids/iiif/20489193/51,426,9,8/full/0/native.jpg)
o
![](https://ids.lib.harvard.edu/ids/iiif/20489193/192,793,26,14/full/0/native.jpg)
xxx
![](https://ids.lib.harvard.edu/ids/iiif/20489193/216,793,23,13/full/0/native.jpg)
2.9
![](https://ids.lib.harvard.edu/ids/iiif/20489193/498,292,129,43/full/0/native.jpg)
GRIST
![](https://ids.lib.harvard.edu/ids/iiif/20489193/626,285,121,43/full/0/native.jpg)
MILL
![](https://ids.lib.harvard.edu/ids/iiif/20489193/504,336,15,14/full/0/native.jpg)
E
![](https://ids.lib.harvard.edu/ids/iiif/20489193/522,333,49,16/full/0/native.jpg)
BEST
![](https://ids.lib.harvard.edu/ids/iiif/20489193/574,330,104,18/full/0/native.jpg)
SUBSTITUTE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/681,328,32,16/full/0/native.jpg)
FOR
![](https://ids.lib.harvard.edu/ids/iiif/20489193/496,346,195,39/full/0/native.jpg)
COFFEE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/506,392,18,16/full/0/native.jpg)
L1
![](https://ids.lib.harvard.edu/ids/iiif/20489193/526,390,21,17/full/0/native.jpg)
BLE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/548,389,54,17/full/0/native.jpg)
BEVERAGE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/508,406,41,14/full/0/native.jpg)
OMACH
![](https://ids.lib.harvard.edu/ids/iiif/20489193/562,404,48,14/full/0/native.jpg)
NERVES
![](https://ids.lib.harvard.edu/ids/iiif/20489193/634,387,15,18/full/0/native.jpg)
IT
![](https://ids.lib.harvard.edu/ids/iiif/20489193/650,386,46,18/full/0/native.jpg)
TASTE
![](https://ids.lib.harvard.edu/ids/iiif/20489193/706,384,44,19/full/0/native.jpg)
GOOD
![](https://ids.lib.harvard.edu/ids/iiif/20489193/849,2,17,14/full/0/native.jpg)
1
![](https://ids.lib.harvard.edu/ids/iiif/20489193/849,10,17,11/full/0/native.jpg)
.
![](https://ids.lib.harvard.edu/ids/iiif/20489193/905,784,48,18/full/0/native.jpg)
9918