Techniques are described for migrating information from a first account to a second account, based on analyzed image(s) of document(s). Image(s) of a document may be generated using an image capture device of a smartphone or other portable computing device. The image(s) may be analyzed, through pattern recognition analysis or barcode scanning, to extract the information from the image(s). The information may then be employed to schedule a transaction, such as payment of a bill described in the information. In some instances, the extracted information may be used as part of an account migration process, in which transactions are migrated from a first account to a second account.

Techniques are described for migrating information from a first account to a second account, based on analyzed image(s) of document(s). Image(s) of a document may be generated using an image capture device of a smartphone or other portable computing device. The image(s) may be analyzed, through pattern recognition analysis or barcode scanning, to extract the information from the image(s). The information may then be employed to schedule a transaction, such as payment of a bill described in the information. In some instances, the extracted information may be used as part of an account migration process, in which transactions are migrated from a first account to a second account.

Image data having text associated with a plurality of text-field types is received, the image data including target image data and context image data. The target image data including target text associated with a text-field type. The context image data providing a context for the target image data. A trained neural network that is constrained to a set of characters for the text-field type is applied to the image data. The trained neural network identifies the target text of the text-field type using a vector embedding that is based on learned patterns for recognizing the context provided by the context image data. One or more predicted characters are provided for the target text of the text-field type in response to identifying the target text using the trained neural network.

Image data having text associated with a plurality of text-field types is received, the image data including target image data and context image data. The target image data including target text associated with a text-field type. The context image data providing a context for the target image data. A trained neural network that is constrained to a set of characters for the text-field type is applied to the image data. The trained neural network identifies the target text of the text-field type using a vector embedding that is based on learned patterns for recognizing the context provided by the context image data. One or more predicted characters are provided for the target text of the text-field type in response to identifying the target text using the trained neural network.

The disclosure herein relates to business content analysis. In particular, the disclosure relates to systems and methods of an expense management system operable to perform automatic business documents' content analysis for generating business reports associated with automated value added tax (VAT) reclaim, Travel and Expenses (T&E) management, Import/Export management and the like. The system is further operable to provide various organizational expense management aspects for the corporate finance department and the business traveler based upon stored data. Additionally, the system is configured to use a content recognition engine, configured as an enhanced OCR mechanism used for extracting tagged text from invoice images and also provides continuous learning mechanism in a structured mode allowing classification of invoice images by type, providing continual process of improvement and betterment throughout.

A machine learning computing system for extracting structured data objects from electronic documents comprising unstructured text includes a first data repository storing a plurality of electronic documents including at least one text data object and an expert system computing device. The expert system computing device includes a processor and a non-transitory memory device storing instructions causing the expert system to receive a first data object comprising unstructured data identified from an electronic document stored in the first data repository, process, a first set of rules to identify at least one key-value pair data object from the first data object; process, by an inference engine module, a second set of rules to identify at least one free text data object from the first data object and store, in a non-transitory memory device, the at least one key-value pair and the at least one free text data object.

A machine learning computing system for extracting structured data objects from electronic documents comprising unstructured text includes a first data repository storing a plurality of electronic documents including at least one text data object and an expert system computing device. The expert system computing device includes a processor and a non-transitory memory device storing instructions causing the expert system to receive a first data object comprising unstructured data identified from an electronic document stored in the first data repository, process, a first set of rules to identify at least one key-value pair data object from the first data object; process, by an inference engine module, a second set of rules to identify at least one free text data object from the first data object and store, in a non-transitory memory device, the at least one key-value pair and the at least one free text data object.

Systems and methods for identifying a presence of a completed document are disclosed. The system may receive an image file from a client device associated with a first document, identify one or more image regions within the image file corresponding to a presence of one or more extractable data entries, selectively extract the one or more extractable data entries, and determine whether the one or more extractable data entries match one or more stored data entries. When the one or more extractable data entries match, the system may determine the status of the first document as completed. When the one or more extractable data entries do not match, the system may proactively replace one or more inconsistent extractable data entries with corresponding stored data entries to form a corrected first document, and generate a request for a client to verify the corrected first document.

Systems and methods to automatically generate classifiers are provided. A labeled dataset is initially received. The dataset may be for a positive class, or may be a negative for a class, or a false positive class. N features that are predictive for the class (or false positive or the negative class) are identified. These features are combined within a classifier dictionary. Medical records received may be processed in order to be machine readable. Features within the medical records are identified and are compared against the dictionary of classifiers. Matches indicate classes within the medical record. The classifier dictionary may be periodically updated in response to insufficient classification accuracy, or when new data becomes available.

Various methods, apparatuses/systems, and media for automatically extracting information from unstructured data are provided. A receiver receives digitized data of a document having unstructured data format. A processor applies machine learning models for sectioning the digitized data. An OCR device applies an OCR processing to the sectioned digitized data. The processor matches the sectioned digitized data to patterns and rules; applies classification models to the matched digitized data to identify entities and events from the sectioned digitized data; automatically link each entity with corresponding event in a hierarchical format to generate a document having structured data format; and output the document having the structured data with metadata having the linked entity with corresponding event in the hierarchical format to downstream applications.