Search Results for author:
Found 24 papers, 15 papers with code
TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal Large Language Models
We conduct extensive experiments on both general and document-oriented MLLM benchmarks, and show that TextHawk outperforms the state-of-the-art methods, demonstrating its effectiveness and superiority in fine-grained document perception and general abilities.
Android in the Zoo: Chain-of-Action-Thought for GUI Agents
To address this, this work presents Chain-of-Action-Thought (dubbed CoAT), which takes the description of the previous actions, the current screen, and more importantly the action thinking of what actions should be performed and the outcomes led by the chosen action.
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.
Joint Learning Neuronal Skeleton and Brain Circuit Topology with Permutation Invariant Encoders for Neuron Classification
Skeleton Encoder integrates the local information of neurons in a bottom-up manner, with a one-dimensional convolution in neural skeleton's point data; Connectome Encoder uses a graph neural network to capture the topological information of neural circuit; finally, Readout Layer fuses the above two information and outputs classification results.
Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach
It incorporates a framewise regularization term in CTC loss to emphasize individual supervision, and leverages the maximizing-a-posteriori of latent alignment to solve the inconsistency problem that arises in distillation between CTC-based models.
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.
Comprehensive Benchmark Datasets for Amharic Scene Text Detection and Recognition
Ethiopic/Amharic script is one of the oldest African writing systems, which serves at least 23 languages (e. g., Amharic, Tigrinya) in East Africa for more than 120 million people.
Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion
By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks.
Ranked #3 on
Scene Text Detection
on MSRA-TD500
SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network
This method constructs the graph by the similarity relationship between cells and adopts GCN to analyze the neighbor embedding information of samples, which makes the similar cell closer to each other on the 2D scatter plot.
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.
Scene Text Detection with Scribble Lines
It is a general labeling method for texts with various shapes and requires low labeling costs.
Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting
Recent end-to-end trainable methods for scene text spotting, integrating detection and recognition, showed much progress.
Ranked #11 on
Text Spotting
on Total-Text
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.
Real-time Scene Text Detection with Differentiable Binarization
Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text.
Ranked #6 on
Scene Text Detection
on MSRA-TD500
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
Moreover, we further investigate the recognition module of our method separately, which significantly outperforms state-of-the-art methods on both regular and irregular text datasets for scene text recognition.
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.
SynthText3D: Synthesizing Scene Text Images from 3D Virtual Worlds
Different from the previous methods which paste the rendered text on static 2D images, our method can render the 3D virtual scene and text instances as an entirety.
Scene Text Recognition from Two-Dimensional Perspective
Inspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem.
Ranked #32 on
Scene Text Recognition
on SVT
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition.
Ranked #3 on
Scene Text Detection
on ICDAR 2013
Rotation-Sensitive Regression for Oriented Scene Text Detection
Previous methods rely on shared features for both tasks, resulting in degraded performance due to the incompatibility of the two tasks.
Ranked #14 on
Scene Text Detection
on MSRA-TD500
TextBoxes++: A Single-Shot Oriented Scene Text Detector
In this paper, we present an end-to-end trainable fast scene text detector, named TextBoxes++, which detects arbitrary-oriented scene text with both high accuracy and efficiency in a single network forward pass.
Ranked #2 on
Scene Text Detection
on COCO-Text
ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
This report introduces RCTW, a new competition that focuses on Chinese text reading.
TextBoxes: A Fast Text Detector with a Single Deep Neural Network
This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression.
