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Link prediction Graph Neural Networks for structure recognition of Handwritten Mathematical Expressions

graph-neural-networks › graph-learning

📄 Abstract

Abstract: We propose a Graph Neural Network (GNN)-based approach for Handwritten Mathematical Expression (HME) recognition by modeling HMEs as graphs, where nodes represent symbols and edges capture spatial dependencies. A deep BLSTM network is used for symbol segmentation, recognition, and spatial relation classification, forming an initial primitive graph. A 2D-CFG parser then generates all possible spatial relations, while the GNN-based link prediction model refines the structure by removing unnecessary connections, ultimately forming the Symbol Label Graph. Experimental results demonstrate the effectiveness of our approach, showing promising performance in HME structure recognition.

Key Contributions

Proposes a GNN-based approach for Handwritten Mathematical Expression (HME) recognition by modeling HMEs as graphs. It combines BLSTM for symbol recognition and spatial relation classification with a GNN-based link prediction model to refine the graph structure, achieving promising performance in HME structure recognition.

Business Value

Enables more accurate digitization and understanding of handwritten mathematical documents, which is valuable for educational platforms, scientific publishing, and historical document analysis.

Paper Metadata

Innovation Type

GNN-based graph modeling for HME structure recognition

Deployment Feasibility

Medium; requires specialized models and potentially significant data preprocessing for handwritten inputs.

Limitations Addressed

Challenges in accurately recognizing the structure of handwritten mathematical expressions, including symbol segmentation, recognition, and spatial relation classification.

Performance Gains

Shows promising performance in HME structure recognition.

Technical Tags

Graph Neural NetworksHandwritten Mathematical ExpressionsStructure RecognitionLink PredictionSymbol SegmentationSpatial RelationsBLSTM2D-CFG ParserSymbol Label GraphGNN

Research Topics

Graph Neural NetworksPattern RecognitionDocument AnalysisSymbol RecognitionStructural Analysis

Methods & Architectures

Graph Neural Network (GNN) based approachModeling HMEs as graphsDeep BLSTM for symbol recognition2D-CFG parserGNN-based link predictionSymbol Label Graph generation Graph Neural Networks (GNNs)Bidirectional Long Short-Term Memory (BLSTM)

Applications & Tasks

Document Analysis Optical Character Recognition (OCR) Mathematical Notation Processing Educational Software Recognizing structure of Handwritten Mathematical Expressions (HMEs)Accurate symbol segmentation and recognitionClassifying spatial relationships between symbols Model HMEs as graphsPerform symbol segmentation and recognitionClassify spatial relationsRefine graph structure using link prediction

Related Fields

Graph Neural NetworksComputer VisionPattern RecognitionNatural Language ProcessingDocument Analysis

Keywords

Graph Neural NetworksHandwritten Mathematical ExpressionsStructure RecognitionLink PredictionSymbol RecognitionSpatial RelationsGNNBLSTMDocument AnalysisOCRGraph ModelingMathematical Notation

Academic Context

#Graph Neural Networks#Pattern Recognition#Document Analysis#Symbol Recognition#Structural Analysis

Commercial Potential

Potential Products

Handwritten math OCR softwareDigital math notebook applicationsTools for analyzing historical mathematical texts

Target Industries

EducationPublishingArchivingSoftware Development

Use Case Examples

Digitizing handwritten math homework for gradingConverting scanned math textbooks into editable formatsEnabling search functionality within handwritten mathematical documents

Competitive Edge

Applies GNNs and link prediction to model the structural relationships in HMEs, offering a novel approach to structure recognition.

Market Opportunity

Niche but important market for specialized OCR and document analysis tools.

Revenue Models

Licensing of recognition enginesdevelopment of specialized software.

Resource Requirements

Compute Needs

Moderate to significant compute for training GNNs and BLSTMs on complex graph structures.

Data Requirements

Requires datasets of handwritten mathematical expressions with symbol and relation annotations.

Deployment Constraints

Accuracy on diverse handwriting styles, computational cost for complex graph processing.

Scalability

Scalability depends on the GNN architecture and the efficiency of graph construction and processing.

Production Readiness

Maturity Level

Research/Development

Time to Market

Medium, requires robust implementation and testing.

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