Information processing system, item value extraction method, and model generation method

An information processing system comprising:

• circuitry configured to:
• acquire a character recognition result that is a result of character recognition performed on a plurality of training images of documents having layouts different from one another;
• generate, for each of character strings included in each of the plurality of training images, the character strings including a character string that is an item value of an extraction target item and other character strings, a feature quantity based on positional relationships between the character string and a plurality of item keywords in the training image, the plurality of item keywords being keyword word strings for use in extraction of the item value of the extraction target item;
• and
• generate a trained model through machine learning, the machine learning being performed using training data,
• the training data associating, for each of the character strings included in each of the plurality of training images, the feature quantity of the character string with information indicating whether the character string is the item value of the extraction target item.

An item value extraction method comprising:

• acquiring a character recognition result that is a result of character recognition performed on a target image;
• extracting, from the character recognition result of the target image, a plurality of candidate character strings that are candidates of an item value of an extraction target item;
• generating, for each of the plurality of candidate character strings, a feature quantity based on positional relationships between the candidate character string and a plurality of item keywords in the target image, the plurality of item keywords being keyword word strings for use in extraction of the item value of the extraction target item;
• storing a trained model in a memory, the trained model being generated through machine learning such that, in response to input of a feature quantity based on positional relationships between a character string and the plurality of item keywords in an image, information indicating appropriateness of the character string being the item value of the extraction target item is output;
• and
• inputting the feature quantity of each of the plurality of candidate character strings in the target image to the trained model so as to extract the item value of the extraction target item from among the plurality of candidate character strings.

A model generation method comprising:

• acquiring a character recognition result that is a result of character recognition performed on a plurality of training images of documents having layouts different from one another;
• generating, for each of character strings included in each of the plurality of training images, the character strings including a character string that is an item value of an extraction target item and other character strings, a feature quantity based on positional relationships between the character string and a plurality of item keywords in the training image, the plurality of item keywords being keyword word strings for use in extraction of the item value of the extraction target item;
• and
• generating a trained model through machine learning, the machine learning being performed using training data,
• the training data associating, for each of the character strings included in each of the plurality of training images, the feature quantity of the character string with information indicating whether the character string is the item value of the extraction target item.