Abstract: A system and method for or facilitating correction of data in documents is disclosed. The method includes receiving one or more documents from and scanning the received one or more documents by using a document processing system for obtaining one or more mis-captured data fields. The method further includes obtaining a historical correction data and determining one or more deltas based on the obtained historical correction data by using a trained data correction-based ML model. Further, the method includes parsing the determined one or more deltas into one or more datasets, generating one or more correct data fields corresponding to the one or more mis-captured data fields and automatically replacing the one or more mis-captured data fields with the generated one or more correct data fields based on one or more predefined rules.

Abstract: Novel tools and techniques are provided for implementing automatic document pattern recognition and analysis. In various embodiments, a computing system might receive one or more first documents containing first remittance information. The computing system might analyze the one or more first documents to extract first remittance information from the first document by obtaining one or more historical documents and correlating the one or more first documents to the one or more historical documents. Next, based on the correlation of the one or more first documents to the one or more historical documents, the computing system might determine at least one historical document that correlates to the one or more first documents. Additionally, the computing system might extract the first remittance information from the one or more first documents based on the at least one historical document.

Abstract: A system and method for or facilitating correction of data in documents is disclosed. The method includes receiving one or more documents from and scanning the received one or more documents by using a document processing system for obtaining one or more mis-captured data fields. The method further includes obtaining a historical correction data and determining one or more deltas based on the obtained historical correction data by using a trained data correction-based ML model. Further, the method includes parsing the determined one or more deltas into one or more datasets, generating one or more correct data fields corresponding to the one or more mis-captured data fields and automatically replacing the one or more mis-captured data fields with the generated one or more correct data fields based on one or more predefined rules.