Search Results for author:
Found 15 papers, 7 papers with code
Robust Scene Text Recognition with Automatic Rectification
We show that the model is able to recognize several types of irregular text, including perspective text and curved text.
Ranked #10 on
Scene Text Recognition
on ICDAR 2003
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.
Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis
In this paper, we investigate the Chinese calligraphy synthesis problem: synthesizing Chinese calligraphy images with specified style from standard font(eg.
Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation
We propose to detect scene text by localizing corner points of text bounding boxes and segmenting text regions in relative positions.
Ranked #2 on
Scene Text Detection
on ICDAR 2017 MLT
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
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
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 #30 on
Scene Text Recognition
on SVT
2D Attentional Irregular Scene Text Recognizer
Irregular scene text, which has complex layout in 2D space, is challenging to most previous scene text recognizers.
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.
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency.
Ranked #1 on
Scene Text Detection
on ICDAR 2015
(Accuracy metric)
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining
Specifically, we transform text data into synthesized text images to unify the data modalities of vision and language, and enhance the language modeling capability of the sequence decoder using a proposed masked image-language modeling scheme.
Single Shot Self-Reliant Scene Text Spotter by Decoupled yet Collaborative Detection and Recognition
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.
Ranked #5 on
Text Spotting
on ICDAR 2015
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.
Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning
Different from existing methods which integrate multiple-granularity features or multiple outputs, we resort to the perspective of representation learning in which auxiliary tasks are utilized to enable the encoder to jointly learn robust features with the main task of per-pixel classification during optimization.
GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction
In this representation, the vertexes and edges of the grid store the localization and adjacency information of the table.
