Search Results for author:
Found 13 papers, 5 papers with code
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.
RD-GAN: Few/Zero-Shot Chinese Character Style Transfer via Radical Decomposition and Rendering
Style transfer has attracted much interest owing to its various applications.
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.
Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model
Despite the success of deep neural network (DNN) on sequential data (i. e., scene text and speech) recognition, it suffers from the over-confidence problem mainly due to overfitting in training with the cross-entropy loss, which may make the decision-making less reliable.
Decoupling Visual-Semantic Feature Learning for Robust Scene Text Recognition
As a result, the learning of semantic features is prone to have a bias on the limited vocabulary of the training set, which is called vocabulary reliance.
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)
+3
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.
Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter
Specifically, we integrate IFA into the two most prevailing text recognition streams (attention-based and CTC-based) and propose attention-guided dense prediction (ADP) and Extended CTC (ExCTC).
Optical Character Recognition
Optical Character Recognition (OCR)
+1
MOST: A Multi-Oriented Scene Text Detector with Localization Refinement
Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios.
Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization
In this framework, two branches named character branch and layout branch are added behind the feature extraction network.
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
An agent network learns from the output of the recognition network and controls the fiducial points to generate more proper training samples for the recognition network.
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting
Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision.
TextField: Learning A Deep Direction Field for Irregular Scene Text Detection
Experimental results show that the proposed TextField outperforms the state-of-the-art methods by a large margin (28% and 8%) on two curved text datasets: Total-Text and CTW1500, respectively, and also achieves very competitive performance on multi-oriented datasets: ICDAR 2015 and MSRA-TD500.
Ranked #22 on
Scene Text Detection
on Total-Text
