Systems and methods for extracting table data from text documents using machine learning are provided. The systems and methods comprise electronically receiving at a computer system a document having one or more tables, each table having one or more whitespace features, processing the document using a first computer model executed by the computer system to classify each row of the one or more tables as a header row or a data row, processing the document using a second computer model executed by the computer system to classify each whitespace feature in each row conditional on classification of each row by the first computer model, the second computer model identifying whether a whitespace feature corresponds to information missing from the one or more tables, and generating an output of the classified whitespace features and storing the output in a digital file.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for electronically extracting table data from text documents using machine learning, comprising: electronically receiving at a computer system a document having one or more tables, each table having one or more whitespace features; processing the document using a first computer model executed by the computer system to classify each row of the one or more tables as a header row or a data row; processing the document using a second computer model executed by the computer system to classify each whitespace feature in each row conditional on classification of each row by the first computer model, the second computer model identifying whether a whitespace feature corresponds to information missing from the one or more tables; and generating an output of the classified whitespace features and storing the output in a digital file. 2. The method of claim 1, wherein the first computer model comprises a random fields classifier. 3. The method of claim 2, wherein the random fields classifier is trained using a set of training tables. 4. The method of claim 1, wherein the second computer model comprises a multinomial logistic classifier. 5. The method of claim 4, wherein the multinomial logistic classifier is trained using a set of training tables. 6. The method of claim 1, wherein the information missing comprises a missing cell. 7. A non-transitory computer-readable medium having computer-readable instructions stored thereon which, when executed by a computer system, cause the computer system to perform the steps of: electronically receiving at a computer system a document having one or more tables, each table having one or more whitespace features; processing the document using a first computer model executed by the computer system to classify each row of the one or more tables as a header row or a data row; processing the document using a second computer model executed by the computer system to classify each whitespace feature in each row conditional on classification of each row by the first computer model, the second computer model identifying whether a whitespace feature corresponds to information missing from the one or more tables; and generating an output of the classified whitespace features and storing the output in a digital file. 8. The non-transitory computer-readable medium of claim 7, wherein the first computer model comprises a random fields classifier. 9. The non-transitory computer-readable medium of claim 8, wherein the random fields classifier is trained using a set of training tables. 10. The non-transitory computer-readable medium of claim 7, wherein the second computer model comprises a multinomial logistic classifier. 11. The non-transitory computer-readable medium of claim 10, wherein the multinomial logistic classifier is trained using a set of training tables. 12. The non-transitory computer-readable medium of claim 7, wherein the information missing comprises a missing cell. 13. A system for electronically extracting table data from text documents using machine learning, comprising: a computer system for electronically receiving a document having one or more tables, each table having one or more whitespace features; an engine executed by the computer system, the engine: processing the document using a first computer model executed by the computer system to classify each row of the one or more tables as a header row or a data row; processing the document using a second computer model executed by the computer system to classify each whitespace feature in each row conditional on classification of each row by the first computer model, the second computer model identifying whether a whitespace feature corresponds to information missing from the one or more tables; and generating an output of the classified whitespace features and storing the output in a digital file. 14. The system of claim 13, wherein the first computer model comprises a random fields classifier. 15. The system of claim 14, wherein the random fields classifier is trained using a set of training tables. 16. The system of claim 13, wherein the second computer model comprises a multinomial logistic classifier. 17. The system of claim 16, wherein the multinomial logistic classifier is trained using a set of training tables. 18. The system of claim 13, wherein the information missing comprises a missing cell.