Automated categorization and assembly of low-quality images into electronic documents

A method comprising:

• applying a machine learning algorithm to a first set of text, to generate a first set of probabilities, wherein:
• the first set of text was generated from a first image of a page of a physical document, wherein the physical document is assigned to a first document category of a set of document categories;
• the machine learning algorithm is configured, when applied to the first set of text, to:
• extract a set of features from the first set of text, the set of features comprising:
• a first plurality of features obtained by performing natural language processing feature extraction operations on the first set of text;
• and
• a second plurality of features, each feature of the second plurality of features assigned to an error type of a set of error types and associated with one or more errors of a set of errors, the one or more errors belonging to the assigned error type of the set of error types;
• generate a first feature vector comprising the first plurality of features and the second plurality of features;
• and
• generate, based on the first feature vector, the first set of probabilities;
• and
• each probability of the first set of probabilities is associated with a document category of the set of document categories and indicates a probability that the physical document from which the first set of text was generated belongs to the associated document category;
• identifying a largest probability of the first set of probabilities;
• and
• assigning the first image to the document category associated with the largest probability of the first set of probabilities.

A system comprising:

• a memory configured to store:
• a first image of a page of a physical document;
• a first machine learning algorithm configured, when executed by a hardware processor, to convert a first image of a page of a physical document into a first set of text, wherein the page comprises a second set of text;
• and
• a second machine learning algorithm configured, when applied to the first set of text and executed by the hardware processor, to:
• extract a set of features from the first set of text, the set of features comprising:
• a first plurality of features obtained by performing natural language processing feature extraction operations on the first set of text;
• and
• a second plurality of features, each feature of the second plurality of features assigned to an error type of a set of error types and associated with one or more errors of a set of errors, the one or more errors of the set of errors belonging to the assigned error type of the set of error types;
• generate a first feature vector comprising the first plurality of features and the second plurality of features;
• and
• generate, based on the first feature vector, a first set of probabilities, each probability of the first set of probabilities associated with a document category of the set of document categories and indicating a probability that the physical document from which the first set of text was generated belongs to the associated document category;
• and
• the hardware processor communicatively coupled to the memory, the hardware processor configured to:
• apply the first machine learning algorithm to the first image, to generate the first set of text;
• apply the second machine learning algorithm to the first set of text, to generate the first set of probabilities;
• identify a largest probability of the first set of probabilities;
• and
• assign the first image to the document category associated with the largest probability of the first set of probabilities.