Cognitive Form Recognizer for Unlabeled Entity Classification
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Solution Overview
Problem
Existing systems struggle to accurately identify and classify form entities such as source and target entities, and to suggest optimal name-value pairs and form designs based on the form type, especially when forms lack explicit labeling.
Innovation Solution
A Form Recognizer Model that utilizes preprocessing techniques, OCR, and a Dynamic Relationship Builder to extract and classify form data, identify name-value pairs, and suggest form designs by analyzing relationships between entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional form processing systems are used, then basic form data extraction is possible, but accurate identification and classification of form entities (source and target entities) cannot be achieved
Solution Approach 1:
The form processing system is divided into distinct modules: pre-processing module, OCR module, form digitization module, form recognition module, and form design recommendation module. Each module handles a specific aspect of form processing, allowing complex tasks to be broken down into manageable segments that can be processed independently and then integrated.
Solution Approach 2:
A dynamic relationship builder is introduced as an intermediary component that analyzes relationships between entities extracted from the form. This intermediary layer processes the raw extracted data and transforms it into classified entity relationships, enabling accurate identification of source and target entities without requiring the entire system to be overly complex.
2Adaptability or versatility
If forms without explicit labeling are processed, then broader form type coverage is achieved, but accurate classification and name-value pair identification becomes difficult
Solution Approach 1:
The form recognition system performs self-service by automatically detecting form types and extracting name-value pairs without requiring explicit labels or manual configuration. The dynamic relationship builder analyzes the structural relationships between extracted entities to infer form types and classify data fields, enabling the system to handle unlabeled forms accurately.
Solution Approach 2:
The system changes the approach from relying on static labels to analyzing dynamic relationships between entities. By examining the contextual relationships and positional information of extracted elements, the system can infer form types and field meanings, adapting to various form types without explicit labeling while maintaining classification accuracy.
3Ease of operation
If scanned forms are converted to digital formats, then form data becomes editable and readable, but data accuracy and positioning may be lost
Solution Approach 1:
The system replaces manual mechanical form filling with an automated digital processing pipeline. The pre-processing and OCR modules convert scanned images into structured digital data, while the form digitization module maintains the spatial relationships and positioning information of the original form, enabling both editability and precision through digital manipulation rather than mechanical copying.
Data Source
AI summary
Systems and methods for a Form Recognizer Model to extract Name-Value pairs automatically and classify the form using the name-value pairs are described. The Name-Value pairs are extracted by identifying Explicit Names and Implicit Names from the pre-processed form. The tables in the form are also extracted. A Dynamic Relationship Builder assists in finding the classification of any unknown form that is fed to the Form Recognizer Model. The proposed model can provide three-fold functionalities, such as digitizing a scanned form into a readable and editable format that can also be stored; identifying the classification of the scanned form; and recommending a list of Name-Value Pairs, Tables and their associated Compositions for designers who would like to design a new form.