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Found 9 papers, 1 papers with code
Towards End-to-End Unified Scene Text Detection and Layout Analysis
In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis.
Unified Line and Paragraph Detection by Graph Convolutional Networks
We formulate the task of detecting lines and paragraphs in a document into a unified two-level clustering problem.
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction
Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks.
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Natural reading orders of words are crucial for information extraction from form-like documents.
Rethinking Text Line Recognition Models
Unlike the more common Transformer-based models, this architecture can handle inputs of arbitrary length, a requirement for universal line recognition.
Post-OCR Paragraph Recognition by Graph Convolutional Networks
We propose a new approach for paragraph recognition in document images by spatial graph convolutional networks (GCN) applied on OCR text boxes.
Towards Unconstrained End-to-End Text Spotting
We propose an end-to-end trainable network that can simultaneously detect and recognize text of arbitrary shape, making substantial progress on the open problem of reading scene text of irregular shape.
Instance Segmentation
Optical Character Recognition (OCR)
+2
A Scalable Handwritten Text Recognition System
These constitute a solution to bring HTR capability into a large scale OCR system.
Sequence-to-Label Script Identification for Multilingual OCR
Therefore we reframe line script identification as a sequence-to-label problem and solve it using two components, trained end-toend: Encoder and Summarizer.
