A Finding Aid for The Equity

2018 ◽  
Author(s):  
Jeff Blackadar
Keyword(s):  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Large Collection ◽  
Digital History ◽  
R Language ◽  
Potential Value ◽  
Optical Character ◽  
Text Searching ◽  
Person Location

Bibliothèque et Archives Nationales du Québec digitally scanned and converted to text a large collection of newspapers to create a resource of tremendous potential value to historians. Unfortunately, the text files are difficult to search reliably due to many errors caused by the optical character recognition (OCR) text conversion process. This digital history project applied natural language processing in an R language computer program to create a new and useful index of this corpus of digitized content despite OCR related errors. The project used editions of The Equity, published in Shawville, Quebec since 1883. The program extracted the names of all the person, location and organization entities that appeared in each edition. Each of the entities was cataloged in a database and related to the edition of the newspaper it appeared in. The database was published to a public website to allow other researchers to use it. The resulting index or finding aid allows researchers to access The Equity in a different way than just full text searching. People, locations and organizations appearing in the Equity are listed on the website and each entity links to a page that lists all of the issues that entity appeared in as well as the other entities that may be related to it. Rendering the text files of each scanned newspaper into entities and indexing them in a database allows the content of the newspaper to be interacted with by entity name and type rather than just a set of large text files. Website: http://www.jeffblackadar.ca/graham_fellowship/corpus_entities_equity/

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 APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION

2021 ◽  
Vol 9 (2) ◽  
pp. 7-12
Author(s):  
Binod Kumar Prasad
Keyword(s):  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Signature Verification ◽  
Optical Character ◽  
Knn Classifier ◽  
Average Recognition Rate ◽  
Distance Coding

Purpose of the study: The purpose of this work is to present an offline Optical Character Recognition system to recognise handwritten English numerals to help automation of document reading. It helps to avoid tedious and time-consuming manual typing to key in important information in a computer system to preserve it for a longer time. Methodology: This work applies Curvature Features of English numeral images by encoding them in terms of distance and slope. The finer local details of images have been extracted by using Zonal features. The feature vectors obtained from the combination of these features have been fed to the KNN classifier. The whole work has been executed using the MatLab Image Processing toolbox. Main Findings: The system produces an average recognition rate of 96.67% with K=1 whereas, with K=3, the rate increased to 97% with corresponding errors of 3.33% and 3% respectively. Out of all the ten numerals, some numerals like ‘3’ and ‘8’ have shown respectively lower recognition rates. It is because of the similarity between their structures. Applications of this study: The proposed work is related to the recognition of English numerals. The model can be used widely for recognition of any pattern like signature verification, face recognition, character or word recognition in another language under Natural Language Processing, etc. Novelty/Originality of this study: The novelty of the work lies in the process of feature extraction. Curves present in the structure of a numeral sample have been encoded based on distance and slope thereby presenting Distance features and Slope features. Vertical Delta Distance Coding (VDDC) and Horizontal Delta Distance Coding (HDDC) encode a curve from vertical and horizontal directions to reveal concavity and convexity from different angles.

scholarly journals Intelligent Short Answer Assessment using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1111-1116
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Teaching Profession ◽  
Student Work ◽  
Short Answer ◽  
Optical Character ◽  
Evaluation Time ◽  
The Cost

Education is fundamental for human progress. A student is evaluated by the mark he/she scores. The evaluation of student’s work is a central aspect of the teaching profession that can affect students in significant ways. Though teachers use multiple criteria for assessing student work, it is not known if emotions are a factor in their grading decisions. Also, there are several mistakes that occur on the department's side like totaling error, marking mistakes. So, we are developing software to automate the evaluation of answers using Natural Language Processing and Machine Learning. There are two modules, in the first module, we use Optical Character Recognition to extract a handwritten font from the uploaded file and the second module evaluates the answer based on various factors and the mark is awarded. For every answer being entered, evaluation is done based on the usage of word, their importance and grammatical meaning of the sentence. With this approach we can save the cost of checking the answers manually and reduce the workload of the teachers by automating the manual checking process. The evaluation time is also reduced by using this software.

scholarly journals Exchanging image processing and OCR components in a Setswana digitisation pipeline

2020 ◽  
Vol 32 (2) ◽  
Author(s):  
Gideon Jozua Kotzé ◽  
Friedel Wolff
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Natural Language Processing ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Character ◽  
Combined Test ◽  
A Current

As more natural language processing (NLP) applications benefit from neural network based approaches, it makes sense to re-evaluate existing work in NLP. A complete pipeline for digitisation includes several components handling the material in sequence. Image processing after scanning the document has been shown to be an important factor in final quality. Here we compare two different approaches for visually enhancing documents before Optical Character Recognition (OCR), (1) a combination of ImageMagick and Unpaper and (2) OCRopus. We also compare Calamari, a new line-based OCR package using neural networks, with the well-known Tesseract 3 as the OCR component. Our evaluation on a set of Setswana documents reveals that the combination of ImageMagick/Unpaper and Calamari improves on a current baseline based on Tesseract 3 and ImageMagick/Unpaper with over 30%, achieving a mean character error rate of 1.69 across all combined test data.

scholarly journals Can Morphological Analyzers Improve the Quality of Optical Character Recognition?

2015 ◽  
pp. 45 ◽  
Author(s):  
Miikka Silfverberg ◽  
Jack Rueter
Keyword(s):  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Language Modeling ◽  
High Coverage ◽  
Optical Character ◽  
Word Lists ◽  
Morphologically Rich Languages ◽  
Digitized Documents

Optical Character Recognition (OCR) can substantially improve the usability of digitized documents. Language modeling using word lists is known to improve OCR quality for English. For morphologically rich languages, however, even large word lists do not reach high coverage on unseen text. Morphological analyzers offer a more sophisticated approach, which is useful in many language processing applications. is paper investigates language modeling in the open-source OCR engine Tesseract using morphological analyzers. We present experiments on two Uralic languages Finnish and Erzya. According to our experiments, word lists may still be superior to morphological analyzers in OCR even for languages with rich morphology. Our error analysis indicates that morphological analyzers can cause a large amount of real word OCR errors.

scholarly journals An efficient extraction of information from Indian Government issued documents Aadhar and Pan Card

2021 ◽  
pp. 56-61
Author(s):  
Rachna Tewani ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Data Collection ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Image Data ◽  
Indian Government ◽  
Use Of Data ◽  
Optical Character ◽  
Efficient Extraction

In today's world, everything is getting digitized, and widespread use of data scanning tools and photography. When we have a lot of image data, it becomes important to accumulate data in a form that is useful for the company/organization. Doing it manually is a tedious task and takes an ample amount of time. Hence to simplify the job, we have developed a FLASK API that takes an image folder as an object and returns an excel sheet of relevant data from the image data. We have used optical character recognition and software like pytesseract to extract data from images. Further in the process, we have used natural language processing, and finally, we have found relevant data using the globe and regex module. This model is helpful in data collection from Registration certificates which helps us store data like chassis number, owner name, car number, etc., easily and can be applied to Aadhaar cards and pan cards.

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