A computer-implemented method used for analyzing documents by an artificial intelligence system is disclosed herein. The method includes uploading at least one document via a frontend which stores the document in a database of the artificial intelligence system; preprocessing at least one document retrieved from the database by splitting the document into individual sentences and calculating a word vector for each sentence of the document; assessing each sentence using a categorization model of the artificial intelligence system by processing the calculated word vector representing the sentence to provide a categorization prediction result; and processing positive categorized sentences in order to calculate an opinion prediction result and a sub-categorization prediction result for each positive categorized sentence based on an opinion prediction model and based on a sub categorization model of the artificial intelligence system.

A system and method for paying bills is disclosed that includes a bill from a payee. The bill includes text payee information that includes a due date, an account number, identification of the payee, an amount due, and payer information. The disclosed system also includes a statement disposed upon the paper bill that includes instructions for a payer to retrieve or take a photo of the bill and to send the picture to a billing text number on the statement. The bill can include one or more images and text. The one or more images can be processed by an image interpretive object and the text payee information can be processed by optical character recognition software for determining payment options that are the most likely to be paid, and the best prediction that the payee will receive payment due.

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by training a pretrained model using a base training dataset including first training datapoints identifying tables in historical document images that include the tables and text, where the generated base model is configured to extract the tables as objects; and generating a table extraction model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.

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.

To perform 3-dimensional interpolation, a 3-dimensional model of an input text character is generated. For example, a 2-dimensional character may be given depth using an extrusion transformation. The 3-dimensional model of the input text character is compared to 3-dimensional models of candidate characters and the results of the 3-dimensional comparisons are used to select the optical character recognition (OCR) output for the input text character. The 3-dimensional comparison may be performed directly on the 3-dimensional models. Alternatively, a set of 2-dimensional images may be generated for each 3-dimensional model and 2-dimensional comparisons performed. By use of the additional information gathered from the comparisons of the 3-dimensional models, the correct OCR output character can be identified with greater confidence. As a result, the quality of the OCR output is improved, improving the functioning of a computer performing OCR tasks and reducing the expenditure of time and processing power in correcting OCR errors.

To perform 3-dimensional interpolation, a 3-dimensional model of an input text character is generated. For example, a 2-dimensional character may be given depth using an extrusion transformation. The 3-dimensional model of the input text character is compared to 3-dimensional models of candidate characters and the results of the 3-dimensional comparisons are used to select the optical character recognition (OCR) output for the input text character. The 3-dimensional comparison may be performed directly on the 3-dimensional models. Alternatively, a set of 2-dimensional images may be generated for each 3-dimensional model and 2-dimensional comparisons performed. By use of the additional information gathered from the comparisons of the 3-dimensional models, the correct OCR output character can be identified with greater confidence. As a result, the quality of the OCR output is improved, improving the functioning of a computer performing OCR tasks and reducing the expenditure of time and processing power in correcting OCR errors.

Systems and methods provide a deployable cloud-agnostic redaction container for performing optical character recognition and redacting information from a document using a cloud-based, guided redaction framework. An example method for document redaction includes receiving a plurality of documents and extracting pages from the plurality of documents. The method then determines, based on a load balancing criterion, a processing order for the pages extracted from the plurality of documents, and performs, based on the processing order, an optical character recognition process and a redaction process on the pages to generate redacted pages. The redacted pages are provided for transmission or storage to a cloud data management platform.

Systems and methods provide a deployable cloud-agnostic redaction container for performing optical character recognition and redacting information from a document using a cloud-based, guided redaction framework. An example method for document redaction includes receiving a plurality of documents and extracting pages from the plurality of documents. The method then determines, based on a load balancing criterion, a processing order for the pages extracted from the plurality of documents, and performs, based on the processing order, an optical character recognition process and a redaction process on the pages to generate redacted pages. The redacted pages are provided for transmission or storage to a cloud data management platform.

A digital camera processing system with software to manage taking photos with a digital camera. Camera software controls the digital camera. A downloaded software component controls the digital camera software and causes a handheld mobile device to perform operations. The operations may include instructing a user to have the digital camera take photos of a check; displaying an instruction on a display of the handheld mobile device to assist the user in having the digital camera take the photos; or assisting the user as to an orientation for taking the photos with the digital camera. The digital camera processing system may generate a log file including a bi-tonal image formatted as a TIFF image.

A computer-implemented method used for analyzing documents by an artificial intelligence system is disclosed herein. The method includes uploading at least one document via a frontend which stores the document in a database of the artificial intelligence system; preprocessing at least one document retrieved from the database by splitting the document into individual sentences and calculating a word vector for each sentence of the document; assessing each sentence using a categorization model of the artificial intelligence system by processing the calculated word vector representing the sentence to provide a categorization prediction result; and processing positive categorized sentences in order to calculate an opinion prediction result and a sub-categorization prediction result for each positive categorized sentence based on an opinion prediction model and based on a sub categorization model of the artificial intelligence system.