Graph Neural Network Line Detection in Unstructured Documents
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Solution Overview
Problem
Current technologies face challenges in automating the detection of lines in unstructured documents, such as receipts, due to issues like image quality, document defects, and variability in layout and orientation, leading to inefficiencies and inaccuracies in data extraction.
Innovation Solution
A graph neural network (GNN) based model is employed to detect lines in images of documents by grouping text segments using a link prediction task, where each text segment is represented as a node, and edges are sampled based on geometric features of bounding boxes, enabling accurate line detection despite variations in document orientation and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional line detection methods are used on unstructured documents, then processing speed may be maintained, but detection accuracy deteriorates due to image quality issues, document defects, and layout variability
Solution Approach 1:
The patent segments the line detection task into multiple processing stages: text detection, text recognition, graph construction, and line detection. This segmentation allows each component to be optimized independently, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary graph structure that connects text detections and text recognitions. This graph serves as a mediator that organizes relationships between detected elements before final line detection, enabling more accurate line identification in unstructured documents with varying layouts.
2Productivity
If manual annotation is used for line detection, then detection accuracy can be maintained, but productivity deteriorates due to high costs and low throughput
Solution Approach 1:
The system employs self-service through automated machine learning models that perform text detection, recognition, and line detection without requiring manual annotation for each document. The model learns from training data and automatically processes unstructured documents, dramatically increasing throughput while maintaining accuracy.
Solution Approach 2:
The patent utilizes parameter changes in the form of trained machine learning model parameters that have been optimized through prior training on labeled data. These pre-trained parameters enable the system to achieve high detection accuracy automatically, eliminating the need for manual annotation during operation while maintaining precision.
3Reliability
If simple detection models are used, then processing speed is maintained, but detection reliability deteriorates in the presence of image quality issues and document defects
Solution Approach 1:
The patent performs preliminary actions by conducting text detection and text recognition before final line detection. This preliminary processing creates a structured graph representation that improves the reliability of subsequent line detection, even in challenging conditions with poor image quality or document defects.
Solution Approach 2:
The system incorporates feedback mechanisms where the graph structure built from text detections and recognitions provides contextual information that feeds into the line detection process. This feedback loop allows the model to adjust and improve detection reliability by considering relationships between detected elements rather than processing them in isolation.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed that detect lines in a document. An example apparatus includes at least one memory; machine readable instructions; and processor circuitry to at least one of instantiate or execute the machine readable instructions to: generate feature embeddings for text segments detected in a document image, the segments associated with respective bounding boxes, wherein the segments are to be represented by nodes in a graph; identify candidate edges between ones of the segments; pass the feature embeddings through graph attention layers (GAT) to update the feature embeddings with information from neighbor nodes; generate an adjacency matrix for the document image by determining confidence scores for the edges; and cluster the nodes based on the adjacency matrix to group ones of the nodes that belong to a same line.