85 citations · 370 across the 35 of their papers we have counts for
29 papers · 1 filter
HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better
Gengluo Li, Xingyu Wan, Shangpin Peng +20
We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image tr…
HyMobileAgent: Data-Environment Co-Scaling for Efficient GUI Agents
Hy Vision Team, Huawen Shen, Zhengyang Tang +20
As large multimodal models move from understanding content to operating on digital environments, mobile GUI has emerged as a challenging and consequential testbed for digital embod…
ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats
Shangpin Peng, Gengluo Li, Xingyu Wan +10
Charts are a primary medium for conveying quantitative and relational information, yet systematically evaluating chart parsing models remains difficult. Existing benchmarks focus o…
Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters
Gengluo Li, Shangpin Peng, Xingyu Wan +16
Vision Large Language Models (VLLMs) have achieved remarkable success in modern text-rich visual understanding. However, their perceptual robustness in the face of the continuous m…
WeCromCL: Weakly Supervised Cross-Modality Contrastive Learning for Transcription-only Supervised Text Spotting
Jingjing Wu, Zhengyao Fang, Pengyuan Lyu +4
Transcription-only Supervised Text Spotting aims to learn text spotters relying only on transcriptions but no text boundaries for supervision, thus eliminating expensive boundary a…
StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyond
Pengyuan Lyu, Yulin Li, Hao Zhou +8
Text-rich images have significant and extensive value, deeply integrated into various aspects of human life. Notably, both visual cues and linguistic symbols in text-rich images pl…
Towards Unified Multi-granularity Text Detection with Interactive Attention
Xingyu Wan, Chengquan Zhang, Pengyuan Lyu +5
Existing OCR engines or document image analysis systems typically rely on training separate models for text detection in varying scenarios and granularities, leading to significant…
GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction
Pengyuan Lyu, Weihong Ma, Hongyi Wang +5
All tables can be represented as grids. Based on this observation, we propose GridFormer, a novel approach for interpreting unconstrained table structures by predicting the vertex…
Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning
Xugong Qin, Pengyuan Lyu, Chengquan Zhang +5
Due to the flexible representation of arbitrary-shaped scene text and simple pipeline, bottom-up segmentation-based methods begin to be mainstream in real-time scene text detection…
MataDoc: Margin and Text Aware Document Dewarping for Arbitrary Boundary
Beiya Dai, Xing li, Qunyi Xie +5
Document dewarping from a distorted camera-captured image is of great value for OCR and document understanding. The document boundary plays an important role which is more evident…
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
Wenwen Yu, Chengquan Zhang, Haoyu Cao +24
Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, an…
Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding
Mingliang Zhai, Yulin Li, Xiameng Qin +6
Transformers achieve promising performance in document understanding because of their high effectiveness and still suffer from quadratic computational complexity dependency on the…
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
Yuechen Yu, Yulin Li, Chengquan Zhang +7
In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-tr…
TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers
Zengyuan Guo, Yuechen Yu, Pengyuan Lv +6
Table structure recognition is a crucial part of document image analysis domain. Its difficulty lies in the need to parse the physical coordinates and logical indices of each cell…
Single Shot Self-Reliant Scene Text Spotter by Decoupled yet Collaborative Detection and Recognition
Jingjing Wu, Pengyuan Lyu, Guangming Lu +2
Typical text spotters follow the two-stage spotting paradigm which detects the boundary for a text instance first and then performs text recognition within the detected regions. De…
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining
Pengyuan Lyu, Chengquan Zhang, Shanshan Liu +7
Text images contain both visual and linguistic information. However, existing pre-training techniques for text recognition mainly focus on either visual representation learning or…
StrucTexT: Structured Text Understanding with Multi-Modal Transformers
Yulin Li, Yuxi Qian, Yuchen Yu +7
Structured text understanding on Visually Rich Documents (VRDs) is a crucial part of Document Intelligence. Due to the complexity of content and layout in VRDs, structured text und…
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
Pengfei Wang, Chengquan Zhang, Fei Qi +7
The reading of arbitrarily-shaped text has received increasing research attention. However, existing text spotters are mostly built on two-stage frameworks or character-based metho…
Learning Global Structure Consistency for Robust Object Tracking
Bi Li, Chengquan Zhang, Zhibin Hong +5
Fast appearance variations and the distractions of similar objects are two of the most challenging problems in visual object tracking. Unlike many existing trackers that focus on m…
Towards Accurate Scene Text Recognition with Semantic Reasoning Networks
Deli Yu, Xuan Li, Chengquan Zhang +3
Scene text image contains two levels of contents: visual texture and semantic information. Although the previous scene text recognition methods have made great progress over the pa…
An End-to-end Video Text Detector with Online Tracking
Hongyuan Yu, Chengquan Zhang, Xuan Li +3
Video text detection is considered as one of the most difficult tasks in document analysis due to the following two challenges: 1) the difficulties caused by video scenes, i.e., mo…
A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning
Pengfei Wang, Chengquan Zhang, Fei Qi +6
Detecting scene text of arbitrary shapes has been a challenging task over the past years. In this paper, we propose a novel segmentation-based text detector, namely SAST, which emp…
Editing Text in the Wild
Liang Wu, Chengquan Zhang, Jiaming Liu +4
In this paper, we are interested in editing text in natural images, which aims to replace or modify a word in the source image with another one while maintaining its realistic look…
Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes
Chengquan Zhang, Borong Liang, Zuming Huang +4
Previous scene text detection methods have progressed substantially over the past years. However, limited by the receptive field of CNNs and the simple representations like rectang…