Search Results for author:
Found 16 papers, 8 papers with code
Sequential Visual and Semantic Consistency for Semi-supervised Text Recognition
Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training.
Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition
By enhancing the alignment between the canonical mask feature and the text feature, the module ensures more effective fusion, ultimately leading to improved recognition performance.
Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution
We evaluate the existing end-to-end methods for VIE on the proposed dataset and observe that the performance of these methods has a distinguishable drop from SROIE (a widely used English dataset) to our proposed dataset due to the larger variance of layout and entities.
Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition
Inspired by the observation that humans learn to recognize the texts through both reading and writing, we propose to learn discrimination and generation by integrating contrastive learning and masked image modeling in our self-supervised method.
Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection
Different from previous approaches that learn robust deep representations of scene text in a holistic manner, our method performs scene text detection based on a few representative features, which avoids the disturbance by background and reduces the computational cost.
Ranked #21 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Improving OCR-Based Image Captioning by Incorporating Geometrical Relationship
Under the guidance of the geometrical relationship between OCR tokens, our LSTM-R capitalizes on a newly-devised relation-aware pointer network to select OCR tokens from the scene text for OCR-based image captioning.
DeepAVO: Efficient Pose Refining with Feature Distilling for Deep Visual Odometry
The technology for Visual Odometry (VO) that estimates the position and orientation of the moving object through analyzing the image sequences captured by on-board cameras, has been well investigated with the rising interest in autonomous driving.
Scene Text Retrieval via Joint Text Detection and Similarity Learning
Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter.
AutoSTR: Efficient Backbone Search for Scene Text Recognition
In this work, inspired by the success of neural architecture search (NAS), which can identify better architectures than human-designed ones, we propose automated STR (AutoSTR) to search data-dependent backbones to boost text recognition performance.
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4.
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.
Symmetry-constrained Rectification Network for Scene Text Recognition
Reading text in the wild is a very challenging task due to the diversity of text instances and the complexity of natural scenes.
ASTER: An Attentional Scene Text Recognizer with Flexible Rectification
SCENE text recognition has attracted great interest from the academia and the industry in recent years owing to its importance in a wide range of applications.
Ranked #21 on
Scene Text Recognition
on ICDAR2015
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Deep-Person: Learning Discriminative Deep Features for Person Re-Identification
Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor.
ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
This report introduces RCTW, a new competition that focuses on Chinese text reading.
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification
Then, we combine the word embedding of the recognized words and the deep visual features into a single representation, which is optimized by a convolutional neural network for fine-grained image classification.
