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Found 38 papers, 31 papers with code
Turning a CLIP Model into a Scene Text Spotter
Utilizing only 10% of the supervised data, FastTCM-CR50 improves performance by an average of 26. 5% and 5. 5% for text detection and spotting tasks, respectively.
ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer
To this end, we introduce a new model named Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter), which achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder.
Box-DETR: Understanding and Boxing Conditional Spatial Queries
In DAB-DETR, such queries are modulated by the so-called conditional linear projection at each decoder stage, aiming to search for positions of interest such as the four extremities of the box.
On Point Affiliation in Feature Upsampling
We introduce the notion of point affiliation into feature upsampling.
ViTEraser: Harnessing the Power of Vision Transformers for Scene Text Removal with SegMIM Pretraining
As ViTEraser implicitly integrates text localization and inpainting, we propose a novel end-to-end pretraining method, termed SegMIM, which focuses the encoder and decoder on the text box segmentation and masked image modeling tasks, respectively.
Looking and Listening: Audio Guided Text Recognition
Text recognition in the wild is a long-standing problem in computer vision.
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
On the Hidden Mystery of OCR in Large Multimodal Models
Large models have recently played a dominant role in natural language processing and multimodal vision-language learning.
ICDAR 2023 Competition on Reading the Seal Title
To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2).
Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation
The recent success of ChatGPT and GPT-4 has drawn widespread attention to multimodal dialogue systems.
Turning a CLIP Model into a Scene Text Detector
Recently, pretraining approaches based on vision language models have made effective progresses in the field of text detection.
Colossal-Auto: Unified Automation of Parallelization and Activation Checkpoint for Large-scale Models
To address these challenges, we introduce a system that can jointly optimize distributed execution and gradient checkpointing plans.
SPTS v2: Single-Point Scene Text Spotting
Within the context of our SPTS v2 framework, our experiments suggest a potential preference for single-point representation in scene text spotting when compared to other representations.
Ranked #14 on
Text Spotting
on ICDAR 2015
Towards Robust Tampered Text Detection in Document Image: New Dataset and New Solution
In this paper, we propose a novel framework to capture more fine-grained clues in complex scenarios for tampered text detection, termed as Document Tampering Detector (DTD), which consists of a Frequency Perception Head (FPH) to compensate the deficiencies caused by the inconspicuous visual features, and a Multi-view Iterative Decoder (MID) for fully utilizing the information of features in different scales.
MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification
MSDS-ChS consists of handwritten Chinese signatures, which, to the best of our knowledge, is the largest publicly available Chinese signature dataset for handwriting verification, at least eight times larger than existing online datasets.
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity.
PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition
Utilizing the proposed weakly supervised learning framework, PageNet requires only transcripts to be annotated for real data; however, it can still output detection and recognition results at both the character and line levels, avoiding the labor and cost of labeling bounding boxes of characters and text lines.
Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context
To address this issue, we propose a Contextual-guided Text Removal Network, termed as CTRNet.
DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation
To the best of our knowledge, we are the first to make a reasonable dynamic runtime scheduler on the combination of tensor swapping and tensor recomputation without user oversight.
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition
End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition.
Ranked #3 on
Text Spotting
on Inverse-Text
SPTS: Single-Point Text Spotting
For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotation of a single-point for each instance.
Ranked #2 on
Text Spotting
on SCUT-CTW1500
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
The success of Transformer models has pushed the deep learning model scale to billions of parameters.
ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment
With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components.
ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting
Previous methods can be roughly categorized into two groups: character-based and segmentation-based, which often require character-level annotations and/or complex post-processing due to the unstructured output.
Ranked #6 on
Text Spotting
on SCUT-CTW1500
Structured Multimodal Attentions for TextVQA
In this paper, we propose an end-to-end structured multimodal attention (SMA) neural network to mainly solve the first two issues above.
ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network
Our contributions are three-fold: 1) For the first time, we adaptively fit arbitrarily-shaped text by a parameterized Bezier curve.
Ranked #9 on
Text Spotting
on Inverse-Text
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering
Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize.
Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild
Furthermore, to tackle the issue of lacking paired training samples, we design an interactive joint training scheme, which shares attention masks from the recognizer to the discriminator, and enables the discriminator to extract the features of each character for further adversarial training.
Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection
More importantly, based on OBD, we provide a detailed analysis of the impact of a collection of refinements, which may inspire others to build state-of-the-art text detectors.
Ranked #3 on
Scene Text Detection
on ICDAR 2017 MLT
ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT
Robust text reading from street view images provides valuable information for various applications.
ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)
This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting.
Omnidirectional Scene Text Detection with Sequential-free Box Discretization
Scene text in the wild is commonly presented with high variant characteristics.
Ranked #1 on
Scene Text Detection
on IC19-ReCTs
(using extra training data)
Aggregation Cross-Entropy for Sequence Recognition
In this paper, we propose a novel method, aggregation cross-entropy (ACE), for sequence recognition from a brand new perspective.
Tightness-aware Evaluation Protocol for Scene Text Detection
Evaluation protocols play key role in the developmental progress of text detection methods.
EnsNet: Ensconce Text in the Wild
The feature of the former is first enhanced by a novel lateral connection structure and then refined by four carefully designed losses: multiscale regression loss and content loss, which capture the global discrepancy of different level features; texture loss and total variation loss, which primarily target filling the text region and preserving the reality of the background.
DeRPN: Taking a further step toward more general object detection
However, the detection performance is sensitive to the setting of the anchor boxes.
Feature Enhancement Network: A Refined Scene Text Detector
In this paper, we propose a refined scene text detector with a \textit{novel} Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement.
Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection
The effectiveness of our approach is evaluated on a public word-level, multi-oriented scene text database, ICDAR 2015 Robust Reading Competition Challenge 4 "Incidental scene text localization".
