Extracting Medical Information from Paper COVID-19 Assessment Forms

2021 ◽  
Vol 12 (01) ◽  
pp. 170-178
Author(s):  
Jacob D. Schultz ◽  
Colin G. White-Dzuro ◽  
Cheng Ye ◽  
Joseph R. Coco ◽  
Janet M. Myers ◽  
...  
Keyword(s):  
User Interface ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Medical Information ◽  
Medical Center ◽  
Data Validation ◽  
Average Accuracy ◽  
Medical Documents ◽  
Optical Mark Recognition ◽  
The Right

Abstract Objective This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center. Methods An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimposed on the left and an editable web form on the right to improve ease of data validation. Crowdsourced participants validated the results of the OMR system. Overall error rate and time taken to validate were calculated. A subset of forms was validated by multiple participants to calculate agreement between participants. Results The OMR/OCR tools correctly extracted data from scanned forms fields with an average accuracy of 70% and median accuracy of 78% when the OMR/OCR results were compared with the results from crowd validation. Scanned forms were crowd-validated at a mean rate of 157 seconds per document and a volume of approximately 108 documents per day. A randomly selected subset of documents was reviewed by multiple participants, producing an interobserver agreement of 97% for documents when narrative-text fields were included and 98% when only Boolean and multiple-choice fields were considered. Conclusion Due to the COVID-19 pandemic, it may be challenging for health care workers wearing personal protective equipment to interact with electronic health records. The combination of OMR/OCR technology, a novel UI, and crowdsourcing data-validation processes allowed for the efficient extraction of a large volume of paper medical documents produced during the COVID-19 pandemic.

HUBUNGAN SIKAP PASIEN TERHADAP HAK DAN KEWAJIBAN RAWAT INAP DI RUMAH SAKIT PEKANBARU MEDICAL CENTER (PMC)

2021 ◽  
Vol 13 (1) ◽  
pp. 17-25
Author(s):  
Nur Maimun ◽  
Arnawilis ◽  
Cindy Feby Fayza ◽  
Nur Asikin
Keyword(s):  
Patient Safety ◽  
Medical Information ◽  
Medical Center ◽  
Patient Treatment ◽  
Patient Attitudes ◽  
Chi Square ◽  
Cross Sectional ◽  
The Right ◽  
Relationship Of ◽  
The Relationship

Patient as service users have right and obligations to be hospitalized and patients also have the right to medical information in receiving medical practice services. This study aims to determine the relationship between patient attitudes towards the rights and obligations of being hospitalized in the hospital Pekanbaru Medical Center (PMC). This research method using observational analytic method with cross sectional design. The total sample used in this study 107 sample were taken as simpel random sampling. Chi square test is used to determine the relationship between variables. The data were processed using SPSS statistical software and analyzed using univariate and bivariate analyzes. Of the result obtained of the study namely the relationship between patient attitude to the rights and obligations of patient with chi-suare obtained pvalue 0.016 (<0,05), the relationship of attitude patients to the rights and obligations of choosing a doctor and class of patient care with chi-square obtained pvalue 0,070 (<0,05), the relationship of patient attitudes to the right and obligations of confidentiality of disease by inpatient medical staff with chi-square obtained pvalue 0,000 (<0,05), the relationship of patient attitudes to the rights and obligations of consent to the patient treatment with chi-square obtained pvalue 0,000 (<0,05), the relationship of patient attitudes to the right and obligation of patient safety with chi-square obtained pvalue 0,000 (<0,05), the relationship between patient attitudes towards the right and obligations of patient safety with chi-square obtained pvalue 0,000 (<0,05). Suggestions in order to protect what has been achieved in this case is his ability as effort of service is getting better in the future Keyword : Attitudes of patient, Rights and Obligations inpatient, Hospital

Finger Spelling in Air System for Deaf and Dumb

2018 ◽  
Vol 9 (1) ◽  
pp. 28-44
Author(s):  
Urmila Shrawankar ◽  
Shruti Gedam
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Human Interaction ◽  
Finger Movements ◽  
Accuracy Rate ◽  
Optical Character ◽  
Average Accuracy ◽  
Bright Color ◽  
Natural Way

Finger spelling in air helps user to operate a computer in order to make human interaction easier and faster than keyboard and touch screen. This article presents a real-time video based system which recognizes the English alphabets and words written in air using finger movements only. Optical Character Recognition (OCR) is used for recognition which is trained using more than 500 various shapes and styles of all alphabets. This system works with different light situations and adapts automatically to various changing conditions; and gives a natural way of communicating where no extra hardware is used other than system camera and a bright color tape. Also, this system does not restrict writing speed and color of tape. Overall, this system achieves an average accuracy rate of character recognition for all alphabets of 94.074%. It is concluded that this system is very useful for communication with deaf and dumb people.

scholarly journals A guide for digitising manuscript climate data

2006 ◽  
Vol 2 (2) ◽  
pp. 137-144 ◽  
Author(s):  
S. Brönnimann ◽  
J. Annis ◽  
W. Dann ◽  
T. Ewen ◽  
A. N. Grant ◽  
...  
Keyword(s):  
Quality Control ◽  
Speech Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Control Strategies ◽  
Climate Data ◽  
Text Data ◽  
Technical Aspects ◽  
Optical Character ◽  
The Right

Abstract. Hand-written or printed manuscript data are an important source for paleo-climatological studies, but bringing them into a suitable format can be a time consuming adventure with uncertain success. Before digitising such data (e.g., in the context a specific research project), it is worthwhile spending a few thoughts on the characteristics of the data, the scientific requirements with respect to quality and coverage, the metadata, and technical aspects such as reproduction techniques, digitising techniques, and quality control strategies. Here we briefly discuss the most important considerations according to our own experience and describe different methods for digitising numeric or text data (optical character recognition, speech recognition, and key entry). We present a tentative guide that is intended to help others compiling the necessary information and making the right decisions.

scholarly journals Implementasi Keras Library dan Convolutional Neural Network Pada Konversi Formulir Pendaftaran Siswa

2019 ◽  
Vol 3 (3) ◽  
pp. 524-531
Author(s):  
Wahyu Andi Saputra ◽  
Muhammad Zidny Naf’an ◽  
Asyhar Nurrochman
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Early Experiment ◽  
Testing Method ◽  
Digital Storage ◽  
Average Accuracy ◽  
Physical Form ◽  
Written Form

Form sheet is an instrument to collect someone’s information and in most cases it is used in a registration or submission process. The challenge being faced by physical form sheet (e.g. paper) is how to convert its content into digital form. As a part of study of computer vision, Optical Character Recognition (OCR) recently utilized to identify hand-written character by learning pattern characteristics of an object. In this research, OCR is implemented to facilitate the conversion of paper-based form sheet's content to be stored properly into digital storage. In order to recognize the character's pattern, this research develops training and testing method in a Convolutional Neural Network (CNN) environment. There are 262.924 images of hand-written character sample and 29 paper-based form sheets from SDN 01 Gumilir Cilacap that implemented in this research. The form sheets also contain various sample of human-based hand-written character. From the early experiment, this research results 92% of accuracy and 23% of loss. However, as the model is implemented to the real form sheets, it obtains average accuracy value of 63%. It is caused by several factors that related to character's morphological feature. From the conducted research, it is expected that conversion of hand-written form sheets become effortless.

scholarly journals Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Salma ◽  
Maham Saeed ◽  
Rauf ur Rahim ◽  
Muhammad Gufran Khan ◽  
Adil Zulfiqar ◽  
...  
Keyword(s):  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Accuracy Score ◽  
Real Time System ◽  
Detection Model ◽  
Average Accuracy ◽  
Future Demands ◽  
Heterogeneous Plate ◽  
Work Done

The metropolis of the future demands an efficient Automatic Number Plate Recognition (ANPR) system. Since every region has a distinct number plate format and style, an unconstrained ANPR system is still not available. There is not much work done on Pakistani number plates because of the unavailability of the data and heterogeneous plate formations. Addressing this issue, we have collected a Pakistani vehicle dataset having various plate configurations and developed a novel ANPR framework using the dataset. The proposed framework localizes the number plate region using the YOLO (You Only Look Once) object detection model, applies robust preprocessing techniques on the extracted plate region, and finally recognizes the plate label using OCR (optical character recognition) Tesseract. The obtained mAP score of the YOLOv3 is 94.3% and the YOLOv4 model is 99.5% on the 0.50 threshold, whereas the average accuracy score of our framework is found to be 73%. For comparison and validation, we implemented a LeNet Convolutional Neural Network (CNN) architecture which uses the segmented image as an input. The comparative analysis shows that the proposed ANPR framework comprising the YOLOv4 and OCR Tesseract has good accuracy and inference time for a wide variation of illumination and style of Pakistani number plates and can be used to develop a real-time system. The proposed ANPR framework will be helpful for researchers developing ANPR for countries having similar challenging vehicle number plate formats and styles.

scholarly journals Kurdish Text Segmentation using Projection-Based Approaches

2021 ◽  
Vol 5 (1) ◽  
pp. 56
Author(s):  
Tofiq Ahmed Tofiq ◽  
Jamal Ali Hussein
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Data Entry ◽  
Vertical Projection ◽  
Data Set ◽  
Main Challenge ◽  
Average Accuracy ◽  
Soft Copy ◽  
Initial Segmentation ◽  
Size Type

An optical character recognition (OCR) system may be the solution to data entry problems for saving the printed document as a soft copy of them. Therefore, OCR systems are being developed for all languages, and Kurdish is no exception. Kurdish is one of the languages that present special challenges to OCR. The main challenge of Kurdish is that it is mostly cursive. Therefore, a segmentation process must be able to specify the beginning and end of the characters. This step is important for character recognition. This paper presents an algorithm for Kurdish character segmentation. The proposed algorithm uses the projection-based approach concepts to separate lines, words, and characters. The algorithm works through the vertical projection of a word and then identifies the splitting areas of the word characters. Then, a post-processing stage is used to handle the over-segmentation problems that occur in the initial segmentation stage. The proposed method is tested using a data set consisting of images of texts that vary in font size, type, and style of more than 63,000 characters. The experiments show that the proposed algorithm can segment Kurdish words with an average accuracy of 98.6%.

scholarly journals Recognition of handwritten Arabic (Indian) numerals using skeleton matching

2020 ◽  
Vol 19 (3) ◽  
pp. 1461
Author(s):  
Bassam Alqaralleh ◽  
Malek Zakarya Alksasbeh ◽  
Tamer Abukhalil ◽  
Harbi Almahafzah ◽  
Tawfiq Al Rawashdeh
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Numerical Data ◽  
Recognition System ◽  
Worst Case ◽  
Limited Success ◽  
Optical Character ◽  
The Right ◽  
Handwritten Arabic

This paper brings into discussion the problem of recognizing Arabic numbers using a monocular camera as the only sensor. When a digital image is presented in this application, optical character recognition (OCR) can be exploited to comprehend numerical data. However, there has been a limited success when applied to the handwritten Arabic (Indian) numbers. This paper aims to overcome this limitation and introduces optical character recognition system based on skeleton matching. The proposed approach is used for handwritten Arabic numbers only. The experimental results indicate the effectiveness of the proposed optical character recognition system even for numbers written in worst case. The right system achieves a recognition rate of 99.3 %.

A Structured Method for the Recognition of Complex Historical Tables

1997 ◽  
Vol 9 (1-3) ◽  
pp. 58-77
Author(s):  
Vitaly Kliatskine ◽  
Eugene Shchepin ◽  
Gunnar Thorvaldsen ◽  
Konstantin Zingerman ◽  
Valery Lazarev
Keyword(s):  
Nineteenth Century ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Source Material ◽  
Historical Sources ◽  
Tax Assessment ◽  
Optical Character ◽  
Algorithmic Model ◽  
Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

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