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.

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.

A system and method for matching a second electronic document to a first electronic document, the first electronic document including at least partially unstructured data of a transaction. The method includes: analyzing the at least partially unstructured data to determine at least one transaction parameter; creating a template for the first electronic document, wherein the template is a structured dataset including the determined at least one transaction parameter; determining, based on the template, a portion of the first electronic document requiring evidence; searching, based on the template, for a second electronic document, wherein the second electronic document indicates of the evidence-requiring portion; and associating the second electronic document with the first electronic document.

The image processing apparatus has an obtaining unit configured to obtain a scanned image, a first determination unit configured to determine a document type of a document format similar to a document format of the scanned image based on information on each registered document type, an extraction unit configured to extract a character string corresponding to a predetermined item, a second determination unit configured to use a different method for determining whether the document format indicated by the scanned image is similar to the document format of the document type determined by the first determination unit in a case where a user modifies the extracted character string, and a display control unit configured to display a screen prompting the user to perform overwriting in a case where the second determination unit determines that the document format is similar.

The image processing apparatus has an obtaining unit configured to obtain a scanned image, a first determination unit configured to determine a document type of a document format similar to a document format of the scanned image based on information on each registered document type, an extraction unit configured to extract a character string corresponding to a predetermined item, a second determination unit configured to use a different method for determining whether the document format indicated by the scanned image is similar to the document format of the document type determined by the first determination unit in a case where a user modifies the extracted character string, and a display control unit configured to display a screen prompting the user to perform overwriting in a case where the second determination unit determines that the document format is similar.

A translation apparatus includes a translation unit which translates content of a document into a different language, a history creating unit which, in translation of the content from a first language into a second language, creates history information including a correspondence between original text in the first language and translated text in the second language, an extraction unit which, in translation of the content from the second language into another language, if content (present content) of the document in the second language is present in the history information, extracts content (absent content) that is not present in the history information, and a combining unit which combines a translation result obtained by translating the present content from the second language into the other language, with a replacement result obtained by replacing the absent content from the second language to the other language based on the history information.

A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.