scholarly journals New Approaches to OCR for Early Printed Books

DigItalia ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 74-87
Author(s):  
Nikolaus Weichselbaumer ◽  
Mathias Seuret ◽  
Saskia Limbach ◽  
Rui Dong ◽  
Manuel Burghardt ◽  
...  
Keyword(s):  
Online Training ◽  
High Accuracy ◽  
Historical Research ◽  
Training Data ◽  
16Th Century ◽  
Second Step ◽  
Printed Books ◽  
Accuracy Level ◽  
Computer Scientists ◽  
Data Book

Books printed before 1800 present major problems for OCR. One of the main obstacles is the lack of diversity of historical fonts in training data. The OCR-D project, consisting of book historians and computer scientists, aims to address this deficiency by focussing on three major issues. Our first target was to create a tool that identifies font groups automatically in images of historical documents. We concentrated on Gothic font groups that were commonly used in German texts printed in the 15th and 16th century: the well-known Fraktur and the lesser known Bastarda, Rotunda, Textura und Schwabacher. The tool was trained with 35,000 images and reaches an accuracy level of 98%. It can not only differentiate between the above-mentioned font groups but also Hebrew, Greek, Antiqua and Italic. It can also identify woodcut images and irrelevant data (book covers, empty pages, etc.). In a second step, we created an online training infrastructure (okralact), which allows for the use of various open source OCR engines such as Tesseract, OCRopus, Kraken and Calamari. At the same time, it facilitates training for specific models of font groups. The high accuracy of the recognition tool paves the way for the unprecedented opportunity to differentiate between the fonts used by individual printers. With more training data and further adjustments, the tool could help to fill a major gap in historical research.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

Headnote Prediction Using Machine Learning

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sarmad Mahar ◽  
Sahar Zafar ◽  
Kamran Nishat
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active Learning ◽  
Text Classification ◽  
Extraction Methods ◽  
Text Summarization ◽  
Training Data ◽  
Second Step ◽  
Support Vector ◽  
Classification Algorithms

Headnotes are the precise explanation and summary of legal points in an issued judgment. Law journals hire experienced lawyers to write these headnotes. These headnotes help the reader quickly determine the issue discussed in the case. Headnotes comprise two parts. The first part comprises the topic discussed in the judgment, and the second part contains a summary of that judgment. In this thesis, we design, develop and evaluate headnote prediction using machine learning, without involving human involvement. We divided this task into a two steps process. In the first step, we predict law points used in the judgment by using text classification algorithms. The second step generates a summary of the judgment using text summarization techniques. To achieve this task, we created a Databank by extracting data from different law sources in Pakistan. We labelled training data generated based on Pakistan law websites. We tested different feature extraction methods on judiciary data to improve our system. Using these feature extraction methods, we developed a dictionary of terminology for ease of reference and utility. Our approach achieves 65% accuracy by using Linear Support Vector Classification with tri-gram and without stemmer. Using active learning our system can continuously improve the accuracy with the increased labelled examples provided by the users of the system.

scholarly journals Design and Development of a 5-Channel Arduino-Based Data Acquisition System (ABDAS) for Experimental Aerodynamics Research

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2382 ◽  
Author(s):  
Antonio Vidal-Pardo ◽  
Santiago Pindado
Keyword(s):  
Data Acquisition ◽  
Low Cost ◽  
Data Acquisition System ◽  
High Accuracy ◽  
Acquisition System ◽  
Calibration System ◽  
Experimental Aerodynamics ◽  
Accuracy Level ◽  
The Fourier Transform ◽  
Cup Anemometer

In this work, a new and low-cost Arduino-Based Data Acquisition System (ABDAS) for use in an aerodynamics lab is developed. Its design is simple and reliable. The accuracy of the system has been checked by being directly compared with a commercial and high accuracy level hardware from National Instruments. Furthermore, ABDAS has been compared to the accredited calibration system in the IDR/UPM Institute, its measurements during this testing campaign being used to analyzed two different cup anemometer frequency determination procedures: counting pulses and the Fourier transform. The results indicate a more accurate transfer function of the cup anemometers when counting pulses procedure is used.

scholarly journals CELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS

2018 ◽  
Author(s):  
Quang Thanh Tran ◽  
Li Jun Hao ◽  
Quang Khai Trinh
Keyword(s):  
Network Traffic ◽  
Cellular Network ◽  
Low Cost ◽  
Real Data ◽  
Exponential Smoothing ◽  
High Accuracy ◽  
Training Data ◽  
Traffic Prediction ◽  
Data Traffic ◽  
Smoothing Methods

Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods. Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic. The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing. By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy.  

scholarly journals 3DLEB-Net: Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at LoD3 Level

2021 ◽  
Vol 11 (19) ◽  
pp. 8996
Author(s):  
Yuwei Cao ◽  
Marco Scaioni
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
Second Step ◽  
Point Cloud Data ◽  
Dynamic Graph ◽  
Cloud Data ◽  
Supervised Methods ◽  
Global And Local

In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmentation network to be applied to point clouds of buildings. However, training such networks requires large amounts of fine-labeled buildings’ point-cloud data, presenting a major challenge in practice because they are difficult to obtain. Consequently, the application of fully supervised DL for semantic segmentation of buildings’ point clouds at LoD3 level is severely limited. In order to reduce the number of required annotated labels, we proposed a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings’ point clouds with limited supervision, named 3DLEB-Net. In general, it consists of two steps. The first step (Autoencoder, AE) is composed of a Dynamic Graph Convolutional Neural Network (DGCNN) encoder and a folding-based decoder. It is designed to extract discriminative global and local features from input point clouds by faithfully reconstructing them without any label. The second step is the semantic segmentation network. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluated our approach based on the Architectural Cultural Heritage (ArCH) dataset. Compared to the fully supervised DL methods, we found that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labeled training data from fully supervised methods as input. Moreover, we conducted a series of ablation studies to show the effectiveness of the design choices of our model.

scholarly journals MULTI-CHANNEL FRY COUNTER DESIGN USING OPTOCOUPLER SENSOR

2019 ◽  
Vol 4 (2) ◽  
pp. 97-104
Author(s):  
Lazuardi Umar ◽  
Yanuar Hamzah ◽  
Rahmondia N. Setiadi
Keyword(s):  
Counting Process ◽  
Production Capacity ◽  
High Accuracy ◽  
Limited Capacity ◽  
High Concentration ◽  
Counting System ◽  
Channel Detection ◽  
Accuracy Level ◽  
High Level ◽  
Almost All

This paper describes a design of a fry counter intended to be used by consuming fish farmer. Along this time, almost all the fry counting process is counted by manual, which is done by a human. It is requiring much energy and needs high concentration; thus, can cause a high level of exhaustion for the fry counting worker. Besides that, the human capability and capacity of counting are limited to a low level. A fry counter design in this study utilizes a multi-channel optocoupler sensor to increase the counting capacity. The multi-channel fry counter counting system is developed as a solution to a limited capacity of available fry counter. This design uses an input signal extender system on controller including the interrupt system. From the experiment, high accuracy level is obtained on the counting and channel detection, therefore, this design can be implemented and could help farmers to increase the production capacity of consuming fish.

scholarly journals Reading and Annotating Galen between 1515–1531: on some Latin Galen Editions in the Library of the Carmelites in Cracow

2019 ◽  
Vol 9 ◽  
pp. 279-300
Author(s):  
Magdalena Koźluk
Keyword(s):  
Intimate Relationship ◽  
The State ◽  
16Th Century ◽  
General State ◽  
Printed Books ◽  
Do So

Copies of early-printed books have been of interest to to-day’s collectors and researchers not only for their material aspects (names of publishers and places of printing, fonts and composition, number of known copies etc.), but also because they bear signs of their often erratic history following their publication. The path followed by a particular copy of an early-printed book is reflected in its general state as an object (for instance the state of its binding), but also in its internal aspect. On the pages of a copy of an early-printed book, annotations, drawings doodles or graphics testify to the intimate relationship that its owners entertained with it. To better understand how owners dealt with copies of the books they possessed, this paper examines the annotations found in copies of some books that belong to the Carmelite convent in Cracow. We hope to bring to the attention of scholars, copies of works of Galen housed in this library, and primarily to set a perspective on how books were read by cultured individuals of in the 16th century period. To do so, we analyse copies of the 1507 Venice edition of the Articella and a copy of Latin edition of Galien (Iuntae, Venice, 1531). We attempt to identify the intellectual perspectives from which cultured readers approached such texts in the 16th century.

scholarly journals Almost Exact Computation of Eigenvalues in Approximate Differential Problems

2019 ◽  
Vol 24 (4) ◽  
pp. 96 ◽  
Author(s):  
José M. A. Matos ◽  
Maria João Rodrigues
Keyword(s):  
Eigenvalue Problems ◽  
High Accuracy ◽  
Second Step ◽  
Matlab Toolbox ◽  
Differential Problem ◽  
Algebraic Problem ◽  
Polynomial Expression ◽  
Computation Of Eigenvalues ◽  
Potential Use ◽  
Mathematics And Physics

Differential eigenvalue problems arise in many fields of Mathematics and Physics, often arriving, as auxiliary problems, when solving partial differential equations. In this work, we present a method for eigenvalues computation following the Tau method philosophy and using Tau Toolbox tools. This Matlab toolbox was recently presented and here we explore its potential use and suitability for this problem. The first step is to translate the eigenvalue differential problem into an algebraic approximated eigenvalues problem. In a second step, making use of symbolic computations, we arrive at the exact polynomial expression of the determinant of the algebraic problem matrix, allowing us to get high accuracy approximations of differential eigenvalues.

Ear biometric verification approach based on morphological and geometric invariants

2020 ◽  
Vol 20 (3) ◽  
pp. 1479
Author(s):  
Wurood A. Jbara
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Main Idea ◽  
High Accuracy ◽  
Geometric Invariants ◽  
Biometric Verification ◽  
Accuracy Level ◽  
Image Capturing ◽  
Verification Process ◽  
Biometric Model

<p>Biometric verification based on ear features is modern filed for scientific research. As known, there are many biometric identifiers that can identify people such as fingerprints, iris and speech. In this paper, the focus is placed on the ear biometric model in order to verifying the identity of persons. The main idea is based on used the moments as ear feature extractors. The proposed approach included some operations as follow: image capturing, edge detection, erosion, feature extraction, and matching. The proposed approach has been tested using many images of the ears with different states. Experimental results using several trails verified that the proposed approach is achieved high accuracy level over a wide variety of ear images. Also, the verification process will be completed by matching query ear image with ear images that kept in database during real time.</p>

Convolutional Neural Network Considering the Effects of Noise for Bearing Fault Diagnosis

2020 ◽  
Author(s):  
Ilyoung Han ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Taeyun Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
High Accuracy ◽  
Training Data ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
System Noise ◽  
Novel Method ◽  
Maintenance Activities

Abstract Convolutional Neural Network (CNN) is, in general, good at finding principal components of data. However, the characteristic components of the signals could often be obscured by system noise. Therefore, even though the CNN model is well-trained and predict with high accuracy, it may detect only the primary patterns of data which could be formed by system noise. They are, in fact, highly vulnerable to maintenance activities such as reassembly. In other words, CNN models could misdiagnose even with excellent performances. In this study, a novel method that combines the classification using CNN with the data preprocessing is proposed for bearing fault diagnosis. The proposed method is demonstrated by the following steps. First, training data is preprocessed so that the noise and the fault signature of the bearings are separated. Then, CNN models are developed and trained to learn significant features containing information of defects. Lastly, the CNN models are examined and validated whether they learn and extract the meaningful features or not.

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