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Found 10 papers, 4 papers with code
An End-to-End OCR Text Re-organization Sequence Learning for Rich-text Detail Image Comprehension
Nowadays rich description on detail images help users know more about the commodities.
Merge and Recognize: A Geometry and 2D Context Aware Graph Model for Named Entity Recognition from Visual Documents
By incorporating geometry information from visual documents into our model, richer 2D context information is generated to improve document representations.
A Simple yet Effective Layout Token in Large Language Models for Document Understanding
Due to the constraint on max position IDs, assigning them to layout information reduces those available for text content, reducing the capacity for the model to learn from the text during training, while also introducing a large number of potentially untrained position IDs during long-context inference, which can hinder performance on document understanding tasks.
SceneVTG++: Controllable Multilingual Visual Text Generation in the Wild
), the text in natural scene images needs to meet the following four key criteria: (1) Fidelity: the generated text should appear as realistic as a photograph and be completely accurate, with no errors in any of the strokes.
WebRPG: Automatic Web Rendering Parameters Generation for Visual Presentation
In the era of content creation revolution propelled by advancements in generative models, the field of web design remains unexplored despite its critical role in modern digital communication.
Visual Text Generation in the Wild
However, it is still challenging to render high-quality text images in real-world scenarios, as three critical criteria should be satisfied: (1) Fidelity: the generated text images should be photo-realistic and the contents are expected to be the same as specified in the given conditions; (2) Reasonability: the regions and contents of the generated text should cohere with the scene; (3) Utility: the generated text images can facilitate related tasks (e. g., text detection and recognition).
LORE: Logical Location Regression Network for Table Structure Recognition
Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats.
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Parsing Table Structures in the Wild
In contrast to existing studies that mainly focus on parsing well-aligned tabular images with simple layouts from scanned PDF documents, we aim to establish a practical table structure parsing system for real-world scenarios where tabular input images are taken or scanned with severe deformation, bending or occlusions.
Graph Convolution for Multimodal Information Extraction from Visually Rich Documents
In VRDs, visual and layout information is critical for document understanding, and texts in such documents cannot be serialized into the one-dimensional sequence without losing information.
