AI Table Extraction From Scanned Financial Statements
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Solution Overview
Problem
Traditional systems and methods are unable to accurately identify and extract key financial information from unstructured or semi-structured financial statements, leading to a manual, time-consuming, and error-prone process that complicates strategic business decision-making.
Innovation Solution
A computer-implemented system using an object detection algorithm and optical character recognition to identify tables in documents, extract text within these tables, and reproduce them in a structured format, utilizing a sliding window technique and natural language processing to refine the table boundaries and headers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to extract financial data from statements, then accuracy can be maintained through human review, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent replaces the mechanical manual process of financial data extraction with an automated computer vision system. The system uses document scanning, preprocessing, table detection algorithms, and OCR technology to automatically extract financial data from statements, eliminating the need for manual human review while maintaining high accuracy through multiple processing stages and validation mechanisms.
Solution Approach 2:
The patent introduces an intermediary automated processing system that acts as a bridge between the unstructured financial statement documents and the structured data required for analysis. This intermediary system includes document scanning, preprocessing, table detection, and data extraction components that transform raw documents into usable structured data, reducing both time and potential errors.
2Productivity
If automated systems are implemented to extract data from financial statements, then processing speed increases, but the systems struggle with non-standard formats and table recognition
Solution Approach 1:
The patent implements a dynamic processing system that can adapt to different document formats. The system uses document scanning to detect the structure of each statement, identifies tables and their boundaries, and adjusts extraction parameters based on the specific format encountered. This dynamic approach allows the system to maintain high processing speeds while handling various non-standard formats effectively.
Solution Approach 2:
The patent employs parameter changes to enhance format adaptability. The system modifies extraction parameters, detection thresholds, and processing settings based on the characteristics of each financial statement format. By dynamically adjusting these parameters, the system can accurately extract data from diverse formats without sacrificing processing speed or requiring complete redesign for each format type.
3Reliability
If manual spreading is performed by credit analysts, then complex financial statements can be interpreted with contextual understanding, but the process is error-prone and costly
Solution Approach 1:
The patent segments the complex financial data extraction process into distinct manageable stages: document scanning, preprocessing, table detection, OCR text extraction, data validation, and structured output generation. This segmentation allows each component to be optimized independently, improving overall reliability while reducing the complexity of the entire system through modular design and automated workflows.
Data Source
AI summary
Consistent with disclosed embodiments, systems, devices, and methods for extracting data from scanned documents using artificial intelligence may be provided. Disclosed embodiments may involve obtaining at least one document associated with at least one entity and identifying at least one table located on the at least one document. Disclosed embodiments may involve extracting text within the identified at least one table based on predicted coordinates of the at least one table and reproducing the at least one table using the extracted text by: identifying at least one header of the at least one table; identifying a final row of the at least one table; identifying, using a sliding window technique, columns of the at least one table; and presenting the extracted text in rows and columns. Disclosed embodiments may store the reproduced at least one table in a database.