Deep-learning-based system and process for image recognition
Disclosed are methods and systems for using artificial intelligence (AI) for image recognition by using predefined coordinates to extract a portion of a received image, the extracted portion comprising a word to be identified having at least a first letter and a second letter; executing an image recognition protocol to identify the first letter; when the server is unable to identify the second letter, the server executes an AI model having a nodal data structure to identify the second letter based upon the identified first letter, the nodal data structure comprising a set of nodes where each node represents a letter, each node connected to at least one other node, wherein connection of a first node to a second node corresponds to a probability that a letter corresponding to the second node is used in a word subsequent to a letter corresponding to the first node.
1. A method comprising:
receiving, by a processor, a request to identify a word depicted within an image, the word having at least a first letter and a second letter subsequent to the first letter;
identifying, by the processor, via executing an image recognition protocol, the first letter; and
when the processor is unable to identify the second letter using the image recognition protocol:
predicting, by the processor, via transmitting the identified first letter and executing a neural network, the second letter based upon the identified first letter by querying a database to retrieve a probability associated with the second letter being used after the identified first letter, the neural network including a set of nodes, each node in the set of nodes interconnected based on the probability associated with the second letter being used after the identified first letter.
2. The method of claim 1 , wherein the neural network predicts the probability that the second letter is used subsequent to the first letter.
3. The method of claim 1 , wherein the processor extracts the word from the image using an optical character recognition protocol.
4. The method of claim 1 , wherein the word is depicted within predefined coordinates of the image.
5. The method of claim 1 , wherein the image is a check image.
6. The method of claim 1 , further comprising:
removing, by the processor, visual noise from the image.
7. The method of claim 6 , wherein the visual noise is a line that is not part of the word.
8. The method of claim 1 , further comprising:
de-slanting, by the processor, at least a portion of the image.
9. The method of claim 1 , further comprising:
training, by the processor, the neural network in accordance with whether the prediction generated by the neural network is correct or incorrect.
10. A system comprising:
a server comprising a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising:
receive a request to identify a word depicted within an image, the word having at least a first letter and a second letter subsequent to the first letter;
identify via executing an image recognition protocol, the first letter; and
when the server is unable to identify the second letter using the image recognition protocol:
predict, via transmitting the identified first letter and executing a neural network, the second letter based upon the identified first letter by querying a database to retrieve a probability associated with the second letter being used after the identified first letter, the neural network including a set of nodes, each node in the set of nodes interconnected based on the probability associated with the second letter being used after the identified first letter.
11. The system of claim 10 , wherein the neural network predicts the probability that the second letter is used subsequent to the first letter.
12. The system of claim 10 , wherein the processor extracts the word from the image using an optical character recognition protocol.
13. The system of claim 10 , wherein the word is depicted within predefined coordinates of the image.
14. The system of claim 10 , wherein the image is a check image.
15. The system of claim 10 , wherein the instructions further cause the processor to:
remove visual noise from the image.
16. The system of claim 15 , wherein the visual noise is a line that is not part of the word.
17. The system of claim 10 , wherein the instructions further cause the processor to:
de-slant at least a portion of the image.
18. The system of claim 10 , wherein the instructions further cause the processor to: train the neural network in accordance with whether the prediction generated by the neural network is correct or incorrect.