We propose a Historical Document Reading Challenge on Large Chinese Structured Family Records, in short ICDAR2019 HDRC CHINESE. The objective of the proposed competition is to recognize and analyze the layout, and finally detect and recognize the textlines and characters of the large historical document collection containing more than 20 000 pages kindly provided by FamilySearch.
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability and complexity, thus posing significant difficulties and challenges for scene text detection and recognition algorithms. The ICDAR 2015 Robust Reading Competition Challenge 4 was launched to assess the performance of existing scene text detection and recognition methods on incidental texts as well as to stimulate novel ideas and solutions. This report is dedicated to briefly introduce our strategies for this challen…
Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community. However, majority of the existing ideas, algorithms and systems are specifically designed for English. This technical report presents the final results of the ICDAR 2015 Text Reading in the Wild (TRW 2015) competition, which aims at establishing a benchmark for assessing detection and recognition algorithms devised for both Chinese and English scripts and providing a playground for researchers from the community. In this article…
Huazhong University of Science and Technology（华中科技大学）；Megvii Technology Inc.（旷视科技有限公司）；Cornell University（康奈尔大学）；Cornell Tech（康奈尔理工学院）；Nanyang Technological University（南洋理工大学）
Chinese is the most widely used language in the world. Algorithms that read Chinese text in natural images facilitate applications of various kinds. Despite the large potential value, datasets and competitions in the past primarily focus on English, which bares very different characteristics than Chinese. This report introduces RCTW, a new competition that focuses on Chinese text reading. The competition features a large-scale dataset with 12,263 annotated images. Two tasks, namely text localization and end-to-end recognition, are set up. The competition took place from January 20 to May 31, 2…
University of Malaya（马来亚大学）；South China University of Technology（华南理工大学）；Baidu Inc（百度公司）；Computer Vision Center, Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机视觉中心）
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. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 - 82.65%, ii) T2.1 - 74.3%, iii) T2.2 - 85.32%, iv) T3.1 - 53.86%, and v) T3.2 - 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants methods. The da…
Meituan-Dianping Group（美团点评集团）；School of EIC, Huazhong University of Science and Technology（华中科技大学EIC学院）；Microsoft Redmond（微软雷德蒙德研究院）；Computer Vision Centre, UAB（巴塞罗那自治大学计算机视觉中心）；Nanyang Technological University（南洋理工大学）；IIIT Hyderabad（印度信息技术研究所海得拉巴分校）
Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinesecharacters can be arranged in various layouts with numerous fonts. The Chinese signboards in street view are a good choice for Chinese scene text images since they have different backgrounds, fonts and layouts. We organized a competition called ICDAR2019-ReCTS, which mainly focuses on reading Chinese text on signboard. This report presents the final results of the competition. A large-…
Huazhong University of Science and Technology（华中科技大学）；Huawei Technologies Ltd.（华为技术有限公司）；Sichuan Optical Character Technology Co., Ltd.（四川光符科技有限公司）；Universitat Autónoma de Barcelona（巴塞罗那自治大学）
University of La Rochelle（拉罗谢尔大学）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Indian Statistical Institute（印度统计研究所）；Czech Technical University（捷克技术大学）；Institute of Automation of Chinese Academy of Sciences（中国科学院自动化研究所）
With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challe…
ICDAR 2023 Video Text Reading Competition for Dense and Small Text
Zhejiang University（浙江大学）；University of Chinese Academy of Sciences（中国科学院大学）；Kuaishou Technology（快手科技）；National University of Singapore（新加坡国立大学）；Indian Statistical Institute（印度统计研究所）；Universitat Autónoma de Barcelona（巴塞罗那自治大学）；Huazhong University of Science and Technology（华中科技大学）
Lip reading aims at decoding texts from the movement of a speaker's mouth. In recent years, lip reading methods have made great progress for English, at both word-level and sentence-level. Unlike English, however, Chinese Mandarin is a tone-based language and relies on pitches to distinguish lexical or grammatical meaning, which significantly increases the ambiguity for the lip reading task. In this paper, we propose a Cascade Sequence-to-Sequence Model for Chinese Mandarin (CSSMCM) lip reading, which explicitly models tones when predicting sentence. Tones are modeled based on visual informati…
A novel method to convert color/multi-spectral images to gray-level images is introduced to increase the performance of document binarization methods. The method uses the distribution of the pixel data of the input document image in a color space to find a transformation, called the dual transform, which balances the amount of information on all color channels. Furthermore, in order to reduce the intensity variations on the gray output, a color reduction preprocessing step is applied. Then, a channel is selected as the gray value representation of the document image based on the homogeneity cr…
Most existing text reading benchmarks make it difficult to evaluate the performance of more advanced deep learning models in large vocabularies due to the limited amount of training data. To address this issue, we introduce a new large-scale text reading benchmark dataset named Chinese Street View Text (C-SVT) with 430,000 street view images, which is at least 14 times as large as the existing Chinese text reading benchmarks. To recognize Chinese text in the wild while keeping large-scale datasets labeling cost-effective, we propose to annotate one part of the CSVT dataset (30,000 images) in l…
Scene text recognition (STR) on Latin datasets has been extensively studied in recent years, and state-of-the-art (SOTA) models often reach high accuracy. However, the performance on non-Latin transcripts, such as Chinese, is not satisfactory. In this paper, we collect six open-source Chinese STR datasets and evaluate a series of classic methods performing well on Latin datasets, finding a significant performance drop. To improve the performance on Chinese datasets, we propose a novel radical-embedding (RE) representation to utilize the ideographic descriptions of Chinese characters. The ideog…
Automatically writing stylized Chinese characters is an attractive yet challenging task due to its wide applicabilities. In this paper, we propose a novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly generate Chinese characters. Specifically, we propose to capture the different characteristics of a Chinese character by disentangling the latent features into content-related and style-related components. Considering of the complex shapes and structures, we incorporate the structure information as prior knowledge into our framework to guide the generation. Our framewo…
National University of Singapore（新加坡国立大学）；Naver Labs（Naver实验室）；University of Rostock（罗斯托克大学）
In this report, we present our findings from benchmarking experiments for information extraction on historical handwritten marriage records Esposalles from IEHHR - ICDAR 2017 robust reading competition. The information extraction is modeled as semantic labeling of the sequence across 2 set of labels. This can be achieved by sequentially or jointly applying handwritten text recognition (HTR) and named entity recognition (NER). We deploy a pipeline approach where first we use state-of-the-art HTR and use its output as input for NER. We show that given low resource setup and simple structure of t…
South China University of Technology（华南理工大学）；Alibaba Group（阿里巴巴集团）
In this paper, we propose an end-to-end trainable framework for restoring historical documents content that follows the correct reading order. In this framework, two branches named character branch and layout branch are added behind the feature extraction network. The character branch localizes individual characters in a document image and recognizes them simultaneously. Then we adopt a post-processing method to group them into text lines. The layout branch based on fully convolutional network outputs a binary mask. We then use Hough transform for line detection on the binary mask and combine …
