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.

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.

ConvNet-Based Optical Recognition for Engineering Drawings

2017 ◽  
Author(s):  
Andrew Brock ◽  
Theodore Lim ◽  
J. M. Ritchie ◽  
Nick Weston
Keyword(s):  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Cross Validation ◽  
Convolutional Networks ◽  
Optical Character ◽  
Engineering Drawings ◽  
Machine Analysis ◽  
Fold Cross Validation ◽  
Optical Recognition

End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.

YOLOv3-Tesseract model for improved intelligent form recognition

2019 ◽  
Vol 13 ◽  
Author(s):  
Zhang Yun-An ◽  
Pan Ziheng ◽  
Dui Hongyan ◽  
Bai Guanghan
Keyword(s):  
Big Data ◽  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Task ◽  
Experimental Simulation ◽  
Form Recognition ◽  
Optical Character

Background: YOLOv3-Tesseract is widely used for the intelligent form recognition because it exhibits several attractive properties. It is important to improve the accuracy and efficiency of the optical character recognition. Methods: The YOLOv3 exhibits the classification advantages for the object detection. Tesseract can effectively recognize regular characters in the field of the optical character recognition. In this study, a YOLOv3 and Tesseract-based model of improved intelligent form recognition is proposed. Results: First, YOLOv3 is trained to detect the position of the text in the table and to subsequently segment text blocks. Second, Tesseract is used to individually detect separated text blocks and combine YOLOv3 and Tesseract to achieve the goal of table character recognition. Conclusion: Based on the Tianchi big data, experimental simulation is used to demonstrate the proposed method. The YOLOv3-Tesseract model is trained and tested to effectively accomplish the recognition task.

scholarly journals Embedded system design to control the entry and exit of vehicles online, at the main access of ESPOCH

2018 ◽  
Vol 3 (CITT2017) ◽  
pp. 113-120
Author(s):  
Javier J. Gavilanes ◽  
Jairo R. Jácome ◽  
Alexandra O. Pazmiño
Keyword(s):  
Embedded System ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Entry And Exit ◽  
Real Time System ◽  
Optical Character ◽  
Nearest Neighbours ◽  
Processing Techniques ◽  
Python Programming ◽  
Main Entrance

In this research a embedded real-time system was developed by using Raspberry Pi3 (a reduced board computer), which is an equipment with a camera placed in strategic points of the mechanic arms at the main entrance and exit of Escuela Superior Politécnica de Chimborazo, this equipment captures images of vehicles that enter and exit the campus and the information is extracted through the implementation of a segmentation algorithm written in Python programming language and the collaboration of artificial vision bookstores offered by OpenCV, processing techniques were applied to extract the vehicle plate from the location scenery. Then, an Optical Character Recognition (OCR) algorithm also known as K-Nearest Neighbours (KNN) was applied, which after a training phase is able to identify letters and numbers on the automobile plates, the information is stored in the entrance database and it is deleted when the automobile exits the campus.

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 U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images

2021 ◽  
Vol 11 (23) ◽  
pp. 11446
Author(s):  
Shih-Hsiung Lee ◽  
Hung-Chun Chen
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Feature Learning ◽  
Single Shot ◽  
Process Data ◽  
Learning Capability ◽  
Detection Model ◽  
Basic Model ◽  
End To End ◽  
Text Content

Tables are an important element in a document and can express more information with fewer words. Due to the different arrangements of tables and texts, as well as the variety of layouts, table detection is a challenge in the field of document analysis. Nowadays, as Optical Character Recognition technology has gradually matured, it can help us to obtain text information quickly, and the ability to accurately detect table structures can improve the efficiency of obtaining text content. The process of document digitization is influenced by the editor’s style on the table layout. In addition, many industries rely on a large number of people to process data, which has high expense, thus, the industry imports artificial intelligence and Robotic Process Automation to handle simple and complicated routine text digitization work. Therefore, this paper proposes an end-to-end table detection model, U-SSD, as based on the object detection method of deep learning, takes the Single Shot MultiBox Detector (SSD) as the basic model architecture, improves it by U-Net, and adds dilated convolution to enhance the feature learning capability of the network. The experiment in this study uses the dataset of accident claim documents, as provided by a Taiwanese Law Firm, and conducts table detection. The experimental results show that the proposed method is effective. In addition, the results of the evaluation on open dataset of TableBank, Github, and ICDAR13 show that the SSD-based network architectures can achieve good performance.

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.

Forgery document detection in information management system using cognitive techniques

2020 ◽  
Vol 39 (6) ◽  
pp. 8057-8068
Author(s):  
Mohamed Sirajudeen ◽  
R. Anitha
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Information Management System ◽  
Detection Methods ◽  
Detection Model ◽  
Identity Card ◽  
Processing Techniques ◽  
Python Programming ◽  
Verification Model

Manually verifying the authenticity of the physical documents (personal identity card, certificates, passports, legal documents) increases the administrative overhead and takes a lot of time. Later image processing techniques were used. But most of the image processing based forgery document detection methods are less accurate. To improve the accuracy, this paper proposes an automatic document verification model using Convolutional Neural Networks (CNN). Furthermore, we use Optical Character Recognition (OCR) and Linear Binary Pattern (LBP) to extract the textual information and regional edges from the documents. Later, Oriented fast and Rotated Brief (ORB) is used to extract the images from the scanned documents. To train the CNN, MIDV-500 dataset of 256 Azerbaijani passport images, each with the size of 1040*744 pixels is taken. The proposed CNN model uses sliding window operations layers to evaluate the authenticity. The proposed model analyzes both the textual authenticity and image (seal, stamp and hologram) authenticity of the scanned document. The experimental analysis is carried out on the TensorFlow using python programming language. The results derived from the proposed CNN based forgery detection model is compared with existing models and the results are promising to implement on the real time applications

Sign in / Sign up

Export Citation Format

Share Document