Automatic document classification using machine learning

A system comprising:

• a user interface;
• a computing device configured to:
• receive, from the user interface, a set of inputs, wherein each input assigns a document of a first set of documents to a classifier of a set of classifiers such that the set of classifiers define document classification rules for a classification model;
• train the classification model using a machine learning technique that assigns each document of a second set of documents to a particular destination of a plurality of destinations based on the document classification rules;
• receive a template design for each destination of the plurality of destinations, wherein each template design specifies metadata to extract for a document type corresponding to documents assigned to a destination;
• subsequently classify a particular document using the classification model, wherein classifying the particular document involves:
• (i) assigning the particular document to a given destination of the plurality of destinations based on the document classification rules, (ii) exporting at least some metadata from the particular document using the template design associated with the given destination, and (iii) obtaining additional information using content of extracted metadata to supplement the exported metadata;
• determine an accuracy rate based on at least manual validation received from a user or automatic validation based on predefined rules;
• and
• provide a notification to the user in response to a determination that the accuracy rate is below a threshold accuracy rate.

An article of manufacture comprising non-transitory data storage storing at least computer-readable instructions that, when executed by one or more processors of a computing device, cause the computing device to perform tasks comprising:

• receiving, from a user interface, a set of inputs, wherein each input assigns a document of a first set of documents to a classifier of a set of classifiers such that the set of classifiers define document classification rules for a classification model;
• training the classification model using a machine learning technique that assigns each document of a second set of documents to a particular destination of a plurality of destinations based on the document classification rules;
• receiving a template design for each destination of the plurality of destinations, wherein each template design specifies metadata to extract for a document type corresponding to documents assigned to a destination;
• subsequently classifying a particular document using the classification model, wherein classifying the particular document involves:
• (i) assigning the particular document to a given destination of the plurality of destinations based on the document classification rules, (ii) exporting at least some metadata from the particular document using the template design associated with the given destination, and (iii) obtaining additional information using content of extracted metadata to supplement the exported metadata;
• determining an accuracy rate based on at least manual validation received from a user or automatic validation based on predefined rules;
• and
• providing a notification to the user in response to a determination that the accuracy rate is below a threshold accuracy rate.