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Found 10 papers, 5 papers with code
A Dataset and Model for Realistic License Plate Deblurring
Vehicle license plate recognition is a crucial task in intelligent traffic management systems.
Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition
The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of OCR.
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds
The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role.
Count, Decode and Fetch: A New Approach to Handwritten Chinese Character Error Correction
This poses a challenge when dealing with an unseen misspelled character, as the decoder may generate an IDS sequence that matches a seen character instead.
HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures
Moreover, we proposed an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem.
SEMv2: Table Separation Line Detection Based on Instance Segmentation
Table structure recognition is an indispensable element for enabling machines to comprehend tables.
Multimodal Tree Decoder for Table of Contents Extraction in Document Images
Next, to parse the hierarchical relationship between the heading entities, a tree-structured decoder is designed.
Multimodal Pre-training Based on Graph Attention Network for Document Understanding
Its main task is to automatically read, understand, and analyze documents.
Split, embed and merge: An accurate table structure recognizer
However, due to the complexity and diversity in their structure and style, it is very difficult to parse the tabular data into the structured format which machines can understand easily, especially for complex tables.
Ranked #8 on
Table Recognition
on PubTabNet
Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring
Non Intrusive Load Monitoring (NILM) or Energy Disaggregation (ED), seeks to save energy by decomposing corresponding appliances power reading from an aggregate power reading of the whole house.
