Overview

• Number of checkpoints: 24

• Number of configs: 20

• Number of papers: 15

  • ALGORITHM: 13

  • BACKBONE: 1

  • PREPROCESSOR: 1

For supported datasets, see datasets overview.

Key Information Extraction Models

• Number of checkpoints: 2

• Number of configs: 2

• Number of papers: 1

  • [ALGORITHM] Spatial Dual-Modality Graph Reasoning for Key Information Extraction (⇨)

Named Entity Recognition Models

• Number of checkpoints: 1

• Number of configs: 1

• Number of papers: 1

  • [ALGORITHM] Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding (⇨)

Text Detection Models

• Number of checkpoints: 12

• Number of configs: 11

• Number of papers: 6

  • [ALGORITHM] Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection (⇨)

  • [ALGORITHM] Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network (⇨)

  • [ALGORITHM] Mask R-CNN (⇨)

  • [ALGORITHM] Real-Time Scene Text Detection With Differentiable Binarization (⇨)

  • [ALGORITHM] Shape Robust Text Detection With Progressive Scale Expansion Network (⇨)

  • [ALGORITHM] Textsnake: A Flexible Representation for Detecting Text of Arbitrary Shapes (⇨)

Text Recognition Models

• Number of checkpoints: 9

• Number of configs: 6

• Number of papers: 7

  • [ALGORITHM] An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition (⇨ ⇨)

  • [ALGORITHM] Nrtr: A No-Recurrence Sequence-to-Sequence Model for Scene Text Recognition (⇨)

  • [ALGORITHM] Robustscanner: Dynamically Enhancing Positional Clues for Robust Text Recognition (⇨)

  • [ALGORITHM] Segocr Simple Baseline. (⇨)

  • [ALGORITHM] Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition (⇨ ⇨)

  • [BACKBONE] Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition (⇨ ⇨)

  • [PREPROCESSOR] Robust Scene Text Recognition With Automatic Rectification (⇨)