scholarly journals AI-Powered Oracle Bone Inscriptions Recognition and Fragments Rejoining

2020 ◽  
Author(s):  
Chongsheng Zhang ◽  
Ruixing Zong ◽  
Shuang Cao ◽  
Yi Men ◽  
Bofeng Mo
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Template Matching ◽  
Large Scale ◽  
Matching Algorithm ◽  
Domain Experts ◽  
Detection Algorithms ◽  
Scene Text Detection ◽  
Large Scale Dataset ◽  
Scene Text

Oracle Bone Inscriptions (OBI) research is very meaningful for both history and literature. In this paper, we introduce our contributions in AI-Powered Oracle Bone (OB) fragments rejoining and OBI recognition. (1) We build a real-world dataset OB-Rejoin, and propose an effective OB rejoining algorithm which yields a top-10 accuracy of 98.39%. (2) We design a practical annotation software to facilitate OBI annotation, and build OracleBone-8000, a large-scale dataset with character-level annotations. We adopt deep learning based scene text detection algorithms for OBI localization, which yield an F-score of 89.7%. We propose a novel deep template matching algorithm for OBI recognition which achieves an overall accuracy of 80.9%. Since we have been cooperating closely with OBI domain experts, our effort above helps advance their research. The resources of this work are available at https://github.com/chongshengzhang/OracleBone.

Robust Scene Text Detection for Multi-script Languages Using Deep Learning

2016 ◽  
pp. 329-340
Author(s):  
Ruo-Ze Liu ◽  
Xin Sun ◽  
Hailiang Xu ◽  
Palaiahnakote Shivakumara ◽  
Feng Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Text Detection ◽  
Scene Text Detection ◽  
Scene Text

scholarly journals Legal Judgment Prediction Based on Multiclass Information Fusion

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kongfan Zhu ◽  
Rundong Guo ◽  
Weifeng Hu ◽  
Zeqiang Li ◽  
Yujun Li
Keyword(s):  
Information Fusion ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
External Information ◽  
Criminal Cases ◽  
Law System ◽  
Large Scale Dataset ◽  
Assistant Systems ◽  
Civil Law System

Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.

scholarly journals Scene text removal via cascaded text stroke detection and erasing

2021 ◽  
Vol 8 (2) ◽  
pp. 273-287
Author(s):  
Xuewei Bian ◽  
Chaoqun Wang ◽  
Weize Quan ◽  
Juntao Ye ◽  
Xiaopeng Zhang ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Processing Unit ◽  
Final Model ◽  
Scene Text ◽  
End To End

AbstractRecent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant results. In this work, a novel end-to-end framework is proposed based on accurate text stroke detection. Specifically, the text removal problem is decoupled into text stroke detection and stroke removal; we design separate networks to solve these two subproblems, the latter being a generative network. These two networks are combined as a processing unit, which is cascaded to obtain our final model for text removal. Experimental results demonstrate that the proposed method substantially outperforms the state-of-the-art for locating and erasing scene text. A new large-scale real-world dataset with 12,120 images has been constructed and is being made available to facilitate research, as current publicly available datasets are mainly synthetic so cannot properly measure the performance of different methods.

scholarly journals Large-scale real-world radio signal recognition with deep learning

2021 ◽  
Author(s):  
Ya Tu ◽  
Yun Lin ◽  
Haoran Zha ◽  
Ju Zhang ◽  
Yu Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Large Scale ◽  
Radio Signal ◽  
Signal Recognition

scholarly journals A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3876 ◽  
Author(s):  
Tiantian Zhu ◽  
Zhengqiu Weng ◽  
Guolang Chen ◽  
Lei Fu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Mobile Devices ◽  
Real World ◽  
Large Scale ◽  
User Authentication ◽  
Mobile User ◽  
Learning System ◽  
Support Vector ◽  
Motion Sensors

With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.

scholarly journals Combined Color Semantics and Deep Learning for the Automatic Detection of Dolphin Dorsal Fins

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 758 ◽  
Author(s):  
Vito Renò ◽  
Gianvito Losapio ◽  
Flavio Forenza ◽  
Tiziano Politi ◽  
Ettore Stella ◽  
...  
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Large Scale ◽  
Binary Classification ◽  
Invasive Technique ◽  
Hybrid Technique ◽  
Domain Experts ◽  
Photo Identification ◽  
Non Invasive ◽  
Biological Studies

Photo-identification is a widely used non-invasive technique in biological studies for understanding if a specimen has been seen multiple times only relying on specific unique visual characteristics. This information is essential to infer knowledge about the spatial distribution, site fidelity, abundance or habitat use of a species. Today there is a large demand for algorithms that can help domain experts in the analysis of large image datasets. For this reason, it is straightforward that the problem of identify and crop the relevant portion of an image is not negligible in any photo-identification pipeline. This paper approaches the problem of automatically cropping cetaceans images with a hybrid technique based on domain analysis and deep learning. Domain knowledge is applied for proposing relevant regions with the aim of highlighting the dorsal fins, then a binary classification of fin vs. no-fin is performed by a convolutional neural network. Results obtained on real images demonstrate the feasibility of the proposed approach in the automated process of large datasets of Risso’s dolphins photos, enabling its use on more complex large scale studies. Moreover, the results of this study suggest to extend this methodology to biological investigations of different species.

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