Extracting data and metadata from identification labels using natural language processing
Methods and systems disclosed herein are directed to detection and recognition of items of data on labels applied to equipment and identifying metadata labels for the items of data using NLP. Embodiments may include identifying one or more items of data on an image of a label associated with a piece of equipment, determining, using NLP on the one or more items of data of the image, one or more metadata associated, respectively, with the identified one or more items of data, and outputting at least one of the one or more metadata and associated items of data.
1. A method comprising:
identifying, by a computing device, one or more items of data on an image of an identification label associated with a piece of equipment;
determining, by the computing device using natural language processing (NLP) on the one or more items of data of the image, one or more metadata associated, respectively, with the identified one or more items of data; and
outputting, by the computing device, at least one of the one or more metadata and associated items of data.
2. The method of claim 1 , wherein an item of data includes text.
3. The method of claim 1 , wherein an item of data includes an indication of a location of the item of data with respect to the identification label.
4. The method of claim 1 , wherein one or more metadata further includes a selected one of: a serial number of the piece of equipment, a model number of the piece of equipment, a part number of the piece of equipment, a rating of the piece of equipment, a manufacturing date of the piece of equipment, a manufacturing location of the piece of equipment, installation instructions for the piece of equipment, operating instructions for the piece of equipment, dimensions of the piece of equipment, and/or power requirements of the piece of equipment.
5. The method of claim 1 , wherein the NLP is associated with a dataset based upon a plurality of items of data associated with a plurality of identification labels associated with a plurality of pieces of equipment, wherein the dataset is used to train the NLP.
6. The method of claim 5 , further comprising:
after outputting, receiving, by the computing device, another one or more metadata associated, respectively, with the identified one or more items of data; and
incorporating, by the computing device, the another one or more metadata associated, respectively, with the identified one or more items of data into the dataset associated with the NLP.
7. The method of claim 6 , wherein the received other one or more metadata associated, respectively, with the identified one or more items of data is based on a review of the one or more metadata associated, respectively, with the identified one or more items of data.
8. The method of claim 1 , wherein identifying the one or more data items further includes applying optical character recognition (OCR) to the image of the identification label.
9. The method of claim 1 , wherein two or more items of data are associated with a metadata.
10. An apparatus comprising:
one or more computer processors;
memory coupled with the one or more computer processors, the memory storing instructions that, when executed by the one or more computer processors, cause the apparatus to:
identify one or more items of data on an image of an identification label associated with a piece of equipment;
determine, using natural language processing (NLP) on the one or more items of data of the image, one or more metadata associated, respectively, with the identified one or more items of data; and
output at least one of the one or more metadata and associated items of data.
11. The apparatus of claim 10 , wherein the NLP is associated with a dataset based upon a plurality of items of data associated with a plurality of identification labels associated with a plurality of pieces of equipment, wherein the dataset is used to train the NLP.
12. The apparatus of claim 11 , further comprising:
after outputting, receiving, by the computing device, another one or more metadata associated, respectively, with the identified one or more items of data; and
incorporating, by the computing device, the another one or more metadata associated, respectively, with the identified one or more items of data into a data set of the NLP for future training.
13. The apparatus of claim 12 , wherein to receive the other one or more metadata associated, respectively, with the identified one or more items of data is based on a review of the one or more metadata associated, respectively, with the identified one or more items of data.
14. The apparatus of claim 10 , wherein to identify the one or more data items further includes to apply optical character recognition (OCR) to the image of the identification label.
15. The apparatus of claim 10 , wherein two or more items of data are associated with a metadata.
16. The apparatus of claim 10 , wherein the apparatus is a mobile computing device.
17. The apparatus of claim 16 , wherein the apparatus further includes an image capturing device coupled with the one or more processors, and wherein the instructions are further to cause the apparatus to capture the image using the image capturing device.
18. A system comprising:
an image capturing device;
a server computing device communicatively coupled with the image capturing device, the server including:
one or more computer processors;
memory coupled with the one or more computer processors, the memory storing instructions that, when executed by the one or more computer processors, cause the server to:
identify one or more items of data on an image of an identification label associated with a piece of equipment;
determine, using natural language processing (NLP) on the one or more items of data of the image, one or more metadata associated, respectively, with the identified one or more items of data; and
output at least one of the one or more metadata and associated items of data.
19. The system of claim 18 , wherein the NLP is associated with a dataset, the dataset based upon a plurality of items of data associated with a plurality of identification labels associated with a plurality of pieces of equipment, and wherein the dataset is used to train the NLP.
20. The system of claim 18 , wherein one or more metadata further includes a selected one of: a serial number of the piece of equipment, a model number of the piece of equipment, a part number of the piece of equipment, a rating of the piece of equipment, a manufacturing date of the piece of equipment, a manufacturing location of the piece of equipment, installation instructions for the piece of equipment, operating instructions for the piece of equipment, dimensions of the piece of equipment, and/or power requirements of the piece of equipment.