Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The text includes errors associated with noise in the image. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the errors from the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: An apparatus includes a memory and processor. The memory stores OCR and NLP algorithms. The processor receives an image of a physical document page and executes the OCR algorithm to convert the image into text. The processor identifies errors in the text, which are associated with noise in the image. The processor generates a feature vector that includes features obtained by executing the NLP algorithm on the text, and features associated with the identified errors in the text. The processor uses the feature vector to assign the image to a document category. Documents assigned to the document category share one or more characteristics, and the feature vector is associated with a probability greater than a threshold that the physical document associated with the image includes those characteristics. The processor then stores the image in a database as a page of an electronic document belonging to the assigned document category.

Abstract: An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The text includes errors associated with noise in the image. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the errors from the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Abstract: An apparatus includes a memory and processor. The memory stores OCR and NLP algorithms. The processor receives an image of a physical document page and executes the OCR algorithm to convert the image into text. The processor identifies errors in the text, which are associated with noise in the image. The processor generates a feature vector that includes features obtained by executing the NLP algorithm on the text, and features associated with the identified errors in the text. The processor uses the feature vector to assign the image to a document category. Documents assigned to the document category share one or more characteristics, and the feature vector is associated with a probability greater than a threshold that the physical document associated with the image includes those characteristics. The processor then stores the image in a database as a page of an electronic document belonging to the assigned document category.