Identifying rendering differences between label rendering engines
First Claim
1. A computing system, comprising:
- at least one processor; and
memory including instructions that, when executed by the processor, cause the computing device to;
determine a magnification level of a region for testing;
generate, for the region at the determined magnification level, at least a first map using a first rendering engine and a second map using a second rendering engine, the first map and the second map each including a plurality of corresponding map labels;
recognize, in each of the first map and the second map, text associated with each of the plurality of map labels using an optical character recognition (OCR) engine; and
compare recognized text of each map label from the first map to each corresponding map label from the second map to identify a level of severity of at least one of an error or an inconsistency, the level of severity based at least in part on at least one of a type of the error or a type of the inconsistency.
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Accused Products
Abstract
Various embodiments provide a method for randomly selecting a region on a map for testing and a map of the region can be generated using multiple map rendering engines. A screenshot of each of the generated maps can be obtained and text associated with map labels, such as street, city, and attraction names, can be recognized using an optical character recognition (OCR) engine. At this point, the recognized text from each rendering engine can then be compared to identify at least one error or inconsistency. In at least one embodiment, categories of errors that need most attention in the specific geographic areas can be identified and a human quality assurance tester can isolate these instances and narrow down the same to identify the rendering or data problem.
18 Citations
20 Claims
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1. A computing system, comprising:
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at least one processor; and memory including instructions that, when executed by the processor, cause the computing device to; determine a magnification level of a region for testing; generate, for the region at the determined magnification level, at least a first map using a first rendering engine and a second map using a second rendering engine, the first map and the second map each including a plurality of corresponding map labels; recognize, in each of the first map and the second map, text associated with each of the plurality of map labels using an optical character recognition (OCR) engine; and compare recognized text of each map label from the first map to each corresponding map label from the second map to identify a level of severity of at least one of an error or an inconsistency, the level of severity based at least in part on at least one of a type of the error or a type of the inconsistency. - View Dependent Claims (2, 3, 4)
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5. A computer-implemented method, comprising:
under the control of one or more computer systems configured with executable instructions, obtaining a first representation of a map from a first map rendering engine and a second representation of the map from a second map rendering engine; recognizing text associated with a plurality of map labels in each of the first representation and the second representation of the map; and comparing the recognized text of the first representation to corresponding recognized text of the second representation of the map to identify a level of severity of at least one inconsistency between the first map rendering engine and the second map rendering engine, the level of severity based at least in part on a type of the inconsistency. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13)
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14. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause a computing device to:
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identify a region on a map to compare a first map rendering engine and a second map rendering engine; obtain a first representation of the map from the first map rendering engine and a second representation of the map from the second map rendering engine; recognize text associated with a plurality of map labels in each of the first representation and the second representation of the map; and compare the recognized text of the first representation to corresponding recognized text of the second representation of the map to identify a level of severity of at least one inconsistency between the first rendering engine and the second map rendering engine, the level of severity based at least in part on a type of the inconsistency. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification