scholarly journals Specimen Data Refinery: A landscape analysis on machine learning, computer vision and automated approaches to capture specimen metadata

2019 ◽  
Vol 3 ◽  
Author(s):  
Laurence Livermore ◽  
Robert Cubey
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Handwriting Recognition ◽  
Landscape Analysis ◽  
Label Data ◽  
Barriers To Access ◽  
Provenance Data ◽  
Segmentation Of Images

Capturing data from specimen images is the most viable way of enriching specimen metadata cheaply and quickly compared to traditional digitisation. Advances in machine learning and computer vision-based tools, and their increasing accessibility and affordability, are greatly increasing the potential to take automated measurements and capture other data from specimens themselves, as well as to transcribe label data. More sophisticated segmentation of images allows us to find parts of interest: particular labels; individual specimens on a slide; or barcodes. Following segmentation, there is the potential to use colour analysis of specimens to perform conditional checking, such as looking for bad cases of verdigris in pinned insects or discoloration of gum-chloral mountant. Automating measurements and landmark analysis of specimens can be used to create trait datasets, all of which will enrich our knowledge of specimens. Segmentation of labels can allow us to cluster similar labels based on their visual properties including colour, shape and patterns—this in turn can be used to make optical character recognition, handwriting recognition and manual transcription much more efficient. Atomising, validating and resolving label data will create structured label data that can be more easily stored, searched and linked to other datasets. We present a landscape analysis on the approaches, summarising previous work, and outline our plan to build future tools and systems in the SYNTHESYS+ Project as part of the Specimen Data Refinery. This will cover the sharing of tools, reducing barriers to access, integrating workflow engines into a software architecture that allows the components to be re-used and re-purposed with provenance data for repeatability, and conforms with the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles (Wilkinson et al. 2016).

scholarly journals Benchmark Pashto Handwritten Character Dataset and Pashto Object Character Recognition (OCR) Using Deep Neural Network with Rule Activation Function

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Imran Uddin ◽  
Dzati A. Ramli ◽  
Abdullah Khan ◽  
Javed Iqbal Bangash ◽  
Nosheen Fayyaz ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Feed Forward Neural Network ◽  
Neural Network Models ◽  
Handwritten Character ◽  
Unseen Data

In the area of machine learning, different techniques are used to train machines and perform different tasks like computer vision, data analysis, natural language processing, and speech recognition. Computer vision is one of the main branches where machine learning and deep learning techniques are being applied. Optical character recognition (OCR) is the ability of a machine to recognize the character of a language. Pashto is one of the most ancient and historical languages of the world, spoken in Afghanistan and Pakistan. OCR application has been developed for various cursive languages like Urdu, Chinese, and Japanese, but very little work is done for the recognition of the Pashto language. When it comes to handwritten character recognition, it becomes more difficult for OCR to recognize the characters as every handwritten character’s shape is influenced by the writer’s hand motion dynamics. The reason for the lack of research in Pashto handwritten character data as compared to other languages is because there is no benchmark dataset available for experimental purposes. This study focuses on the creation of such a dataset, and then for the evaluation purpose, a machine is trained to correctly recognize unseen Pashto handwritten characters. To achieve this objective, a dataset of 43000 images was created. Three Feed Forward Neural Network models with backpropagation algorithm using different Rectified Linear Unit (ReLU) layer configurations (Model 1 with 1-ReLU Layer, Model 2 with 2-ReLU layers, and Model 3 with 3-ReLU Layers) were trained and tested with this dataset. The simulation shows that Model 1 achieved accuracy up to 87.6% on unseen data while Model 2 achieved an accuracy of 81.60% and 3% accuracy, respectively. Similarly, loss (cross-entropy) was the lowest for Model 1 with 0.15 and 3.17 for training and testing, followed by Model 2 with 0.7 and 4.2 for training and testing, while Model 3 was the last with loss values of 6.4 and 3.69. The precision, recall, and f-measure values of Model 1 were better than those of both Model 2 and Model 3. Based on results, Model 1 (with 1 ReLU activation layer) is found to be the most efficient as compared to the other two models in terms of accuracy to recognize Pashto handwritten characters.

Machine Learning Techniques Application

2021 ◽  
pp. 1396-1417
Author(s):  
Karthikeyan P. ◽  
Karunakaran Velswamy ◽  
Pon Harshavardhanan ◽  
Rajagopal R. ◽  
JeyaKrishnan V. ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Modern World ◽  
Interdisciplinary Field ◽  
Sound Image ◽  
Learning Techniques

Machine learning is the part of artificial intelligence that makes machines learn without being expressly programmed. Machine learning application built the modern world. Machine learning techniques are mainly classified into three techniques: supervised, unsupervised, and semi-supervised. Machine learning is an interdisciplinary field, which can be joined in different areas including science, business, and research. Supervised techniques are applied in agriculture, email spam, malware filtering, online fraud detection, optical character recognition, natural language processing, and face detection. Unsupervised techniques are applied in market segmentation and sentiment analysis and anomaly detection. Deep learning is being utilized in sound, image, video, time series, and text. This chapter covers applications of various machine learning techniques, social media, agriculture, and task scheduling in a distributed system.

scholarly journals Image Spam Detection Using Machine Learning and Natural Language Processing

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Yaseen Khather Yaseen ◽  
Alaa Khudhair Abbas ◽  
Ahmed M. Sana
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Optical Character ◽  
Harmful Content

Today, images are a part of communication between people. However, images are being used to share information by hiding and embedding messages within it, and images that are received through social media or emails can contain harmful content that users are not able to see and therefore not aware of. This paper presents a model for detecting spam on images. The model is a combination of optical character recognition, natural language processing, and the machine learning algorithm. Optical character recognition extracts the text from images, and natural language processing uses linguistics capabilities to detect and classify the language, to distinguish between normal text and slang language. The features for selected images are then extracted using the bag-of-words model, and the machine learning algorithm is run to detect any kind of spam that may be on it. Finally, the model can predict whether or not the image contains any harmful content. The results show that the proposed method using a combination of the machine learning algorithm, optical character recognition, and natural language processing provides high detection accuracy compared to using machine learning alone.

scholarly journals Computer Vision for Continuous Bedside Pharmacological Data Extraction: A Novel Application of Artificial Intelligence for Clinical Data Recording and Biomedical Research

2021 ◽  
Vol 4 ◽  
Author(s):  
Logan Froese ◽  
Joshua Dian ◽  
Carleen Batson ◽  
Alwyn Gomez ◽  
Amanjyot Singh Sainbhi ◽  
...  
Keyword(s):  
Computer Vision ◽  
Data Integration ◽  
Real Time ◽  
Clinical Data ◽  
Biomedical Research ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Time Data ◽  
Optical Character ◽  
Real Time Data

Introduction: As real time data processing is integrated with medical care for traumatic brain injury (TBI) patients, there is a requirement for devices to have digital output. However, there are still many devices that fail to have the required hardware to export real time data into an acceptable digital format or in a continuously updating manner. This is particularly the case for many intravenous pumps and older technological systems. Such accurate and digital real time data integration within TBI care and other fields is critical as we move towards digitizing healthcare information and integrating clinical data streams to improve bedside care. We propose to address this gap in technology by building a system that employs Optical Character Recognition through computer vision, using real time images from a pump monitor to extract the desired real time information.Methods: Using freely available software and readily available technology, we built a script that extracts real time images from a medication pump and then processes them using Optical Character Recognition to create digital text from the image. This text was then transferred to an ICM + real-time monitoring software in parallel with other retrieved physiological data.Results: The prototype that was built works effectively for our device, with source code openly available to interested end-users. However, future work is required for a more universal application of such a system.Conclusion: Advances here can improve medical information collection in the clinical environment, eliminating human error with bedside charting, and aid in data integration for biomedical research where many complex data sets can be seamlessly integrated digitally. Our design demonstrates a simple adaptation of current technology to help with this integration.

scholarly journals Transcript Anatomization with Multi-Linguistic and Speech Synthesis Features

2021 ◽  
Vol 9 (VI) ◽  
pp. 1755-1758
Author(s):  
Rohan Modi
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Handwriting Recognition ◽  
Cost Effective ◽  
Computer Hardware ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Audio Output

Handwriting Detection is a process or potential of a computer program to collect and analyze comprehensible input that is written by hand from various types of media such as photographs, newspapers, paper reports etc. Handwritten Text Recognition is a sub-discipline of Pattern Recognition. Pattern Recognition is refers to the classification of datasets or objects into various categories or classes. Handwriting Recognition is the process of transforming a handwritten text in a specific language into its digitally expressible script represented by a set of icons known as letters or characters. Speech synthesis is the artificial production of human speech using Machine Learning based software and audio output based computer hardware. While there are many systems which convert normal language text in to speech, the aim of this paper is to study Optical Character Recognition with speech synthesis technology and to develop a cost effective user friendly image based offline text to speech conversion system using CRNN neural networks model and Hidden Markov Model. The automated interpretation of text that has been written by hand can be very useful in various instances where processing of great amounts of handwritten data is required, such as signature verification, analysis of various types of documents and recognition of amounts written on bank cheques by hand.

scholarly journals A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources

2015 ◽  
Vol 2 ◽  
pp. 1-19
Author(s):  
Gunnar Thorvaldsen ◽  
Joana Maria Pujadas-Mora ◽  
Trygve Andersen ◽  
Line Eikvil ◽  
Josep Lladós ◽  
...  
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Handwriting Recognition ◽  
Data Entry ◽  
Optimal Solution ◽  
Historical Sources ◽  
Cross Sectional ◽  
Vital Records ◽  
Printed Materials ◽  
Source Materials

This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources.

scholarly journals OCR Text Extraction

2021 ◽  
Vol 11 (2) ◽  
pp. 83-86
Author(s):  
Alan Jiju ◽  
Shaun Tuscano ◽  
Chetana Badgujar
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Text Segmentation ◽  
Similar Data ◽  
Written Text ◽  
Amount Of Information ◽  
Optical Character ◽  
Intermediate Image

This research tries to find out a methodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on – such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.

scholarly journals A Study of Novel Optical Character Recognition Algorithms

2021 ◽  
Vol 23 (06) ◽  
pp. 301-305
Author(s):  
Roshan Suvaris ◽  
◽  
Dr. S Sathyanarayana ◽  
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Human Life ◽  
Handwriting Recognition ◽  
Digital Storage ◽  
Optical Character ◽  
Pros And Cons ◽  
Computer Text ◽  
Almost All ◽  
Textual Content

Optical Character Recognition has been an inseparable part of human life during everyday transactions. The OCR has extended its application areas in almost all fields viz. healthcare, finance, banking, entertainment, trading system, digital storage, and so on. In the recent past, handwriting recognition is one of the hardest study areas in the area of image processing. In this paper, the various techniques for converting textual content from number plates, printed, handwritten paper documents into machine code have been discussed. The transforming method used in all these techniques is known as OCR. The English OCR system is necessary for the conversion of various published books and other documents in English into human editable computer text files. The latest researches in this area have included methodologies that identify different fonts and styles of English handwritten scripts. As of date, even though a number of algorithms are available, it has its own pros and cons. Since the recognition of different styles and fonts in machine-printed and handwritten English script is the biggest challenge, this field is open for researchers to implement new algorithms that would overcome the deficiencies of its predecessors.

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