Continuous learning for document processing and analysis
A document processing method includes receiving one or more documents, performing optical character recognition on the one or more documents to detect words comprising symbols in the one or more documents, and determining a encoding value for each of the symbols. It further includes applying a first hash function to each encoding value to generate a first set of hashed symbol values, applying a second hash function to each hashed symbol value to generate a vector array including a second set of hashed symbol values, and applying a linear transformation to each value of the second set of hashed symbol values of the vector array. The method also includes applying an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols and saving the abstract values to train a neural network to detect fields in an input document.
1. A method comprising:
receiving, by a processing device, one or more documents;
performing optical character recognition on the one or more documents to detect words comprising symbols in the one or more documents;
determining an encoding value for each of the symbols;
applying a first hash function to each encoding value to generate a first set of hashed symbol values;
applying a second hash function to each hashed symbol value of the first set of hashed symbol values to generate a vector array comprising a second set of hashed symbol values;
applying a linear transformation to each value of the second set of hashed symbol values of the vector array;
applying an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols; and
saving the abstract values to train a neural network to detect fields in an input document.
2. The method of claim 1 , further comprising training a neural network to detect fields in the input document using the saved abstract values.
3. The method of claim 1 , wherein the encoding value is a Unicode value.
4. The method of claim 1 , wherein the first hash function is a summation of a whole number and a remainder of a division of the encoding value and the whole number.
5. The method of claim 1 , wherein the vector array encodes information associated with features of the words.
6. The method of claim 1 , wherein the second hash function is different from the first hash function.
7. The method of claim 1 , wherein the first hash function is a universal hash function.
8. A system comprising:
a memory;
a processor coupled to the memory, the processor configured to:
receive one or more documents;
perform optical character recognition on the one or more documents to detect words comprising symbols in the one or more documents;
determine a encoding value for each of the symbols;
apply a first hash function to each encoding value to generate a first set of hashed symbol values;
apply a second hash function to each hashed symbol value of the first set of hashed symbol values to generate a vector array comprising a second set of hashed symbol values;
apply a linear transformation to each value of the second set of hashed symbol values of the vector array;
apply an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols; and
save, to the memory, the abstract values to train a neural network to detect fields in an input document.
9. The system of claim 8 , wherein the processor is further configured to train a neural network to detect fields in the input document using the saved abstract values.
10. The system of claim 8 , wherein the encoding value is a Unicode value.
11. The system of claim 8 , wherein the first hash function is a summation of a whole number and a remainder of a division of the encoding value and the whole number.
12. The system of claim 8 , wherein the vector array encodes information associated with features of the words.
13. The system of claim 8 , wherein the second hash function is different from the first hash function.
14. The system of claim 8 , wherein the first hash function is a universal hash function.
15. A non-transitory machine-readable storage medium including instructions that, when accessed by a processing device, cause the processing device to:
receive one or more documents;
perform optical character recognition on the one or more documents to detect words comprising symbols in the one or more documents;
determine a encoding value for each of the symbols;
apply a first hash function to each encoding value to generate a first set of hashed symbol values;
apply a second hash function to each hashed symbol value of the first set of hashed symbol values to generate a vector array comprising a second set of hashed symbol values;
apply a linear transformation to each value of the second set of hashed symbol values of the vector array;
apply an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols; and
save, to a memory, the abstract values to train a neural network to detect fields in an input document.
16. The non-transitory machine-readable storage medium of claim 15 , wherein the instructions further cause the processing device to train a neural network to detect fields in the input document using the saved abstract values.
17. The non-transitory machine-readable storage medium of claim 15 , wherein the encoding value is a Unicode value.
18. The non-transitory machine-readable storage medium of claim 15 , wherein the first hash function is a summation of a whole number and a remainder of a division of the encoding value and the whole number.
19. The non-transitory machine-readable storage medium of claim 15 , wherein the vector array encodes information associated with features of the words.
20. The non-transitory machine-readable storage medium of claim 15 , wherein the second hash function is different from the first hash function.