Abstract: Aspects of the present disclosure are directed to extracting textual information from image documents. In one embodiment, a system, upon receiving a request to extract textual information from an image document, a digital processing system performs character recognition based on content of the image document using multiple approaches to generate corresponding texts. The texts are then combined to determine a result text representing the textual information contained in the image document. The result is then provided as a response to the request.

Abstract: Systems, methods, and other embodiments associated with identifying a document type of an unidentified document based on image features are described. In one embodiment, image pairs are formed by pairing the unidentified document with anchor images from a plurality of anchor images, wherein each anchor image is a known document type. For each image pair, first visual features are extracted from the unidentified document and second visual features are extracted from the paired anchor image. A similarity function is applied to compare the first visual features and the second visual features, and a similarity score is generated for each image pair based on the comparing. The most similar anchor image from the image pairs, which has a greatest similarity score, is identified. The document type of the unidentified document is then predicted as the known document type associated with the most similar anchor image.

Abstract: Systems, methods, and other embodiments associated with a data quality framework for a machine learning pipeline are described. In one embodiment, a method includes receiving an input dataset prior to training a machine learning model with the input dataset. The input dataset, having data records in column tabular form, is analyzed to determine a data quality of the input dataset prior to training the machine learning model. A data quality score is generated that represents an overall quality of the input dataset, where the data quality score is generated based on a combined ensemble of at least two factors selected from (i) an abundance factor, (ii) a completeness factor, and (iii) a dimension efficiency factor. Based on the data quality score, the input dataset is either permitted or prohibited from continuing in the machine learning pipeline, and corrective actions may be performed.

Abstract: Method includes generating a base model by training a pretrained model using a base training dataset including first training datapoints identifying tables in historical document images that include the tables and text, where the generated base model is configured to extract the tables as objects; and generating a table extraction model by training the base model using an enhanced training dataset including second training datapoints that are different from the first training datapoints and identify a plurality of cells disposed in each of the tables in a row direction and a column direction. The table extraction model is trained to output content of the tables and table information in an XML format, the table information including cell level information of the plurality of cells that is searchable via a query configured to provide target content that corresponds to one or more cells.

Abstract: A bill verification system for verifying bill records associated with an entity account is disclosed. The system receives a request to verify a bill record associated with an entity account. The system identifies a bill level model to be used for verifying the bill record and detects based on the bill level model, bill level anomaly information for the bill record. The system additionally identifies a bill line level model to be used for verifying one or more bill lines in the bill record and detects based on the bill line level model, bill line level anomaly information for the bill record. The system then aggregates the bill level anomaly information and the bill line level anomaly information to generate a bill verification report for the bill record. The system provides the bill verification report as a response to the request received to verify the bill record.

Abstract: According to an aspect, a technology system maintains an account data specifying the details of multiple accounts including a specific set of accounts as being delinquent. Upon receiving a feature set to be used, the system clusters the multiple accounts into groups based on the feature set. The system generates a multi-classification model for assignment of agents to the accounts based on the group qualifiers associated with the groups and the feature set. The system then identifies a target group likely to present problems with debt collection, assigns, using the multi-classification model, a corresponding agent for each account of the group and determines a recommended recovery strategy for each assignment. The system provides the details of the group, the corresponding agents assigned and the recommended recovery strategy for each assignment.