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.

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.

Kurzweil Reading Machine: A Partial Evaluation of Its Optical Character Recognition Error Rate

1979 ◽  
Vol 73 (10) ◽  
pp. 389-399
Author(s):  
Gregory L. Goodrich ◽  
Richard R. Bennett ◽  
William R. De L'aune ◽  
Harvey Lauer ◽  
Leonard Mowinski
Keyword(s):  
Error Rate ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Partial Evaluation ◽  
Error Rates ◽  
Recognition Error ◽  
Optical Character ◽  
Printed Materials ◽  
High Level

This study was designed to assess the Kurzweil Reading Machine's ability to read three different type styles produced by five different means. The results indicate that the Kurzweil Reading Machines tested have different error rates depending upon the means of producing the copy and upon the type style used; there was a significant interaction between copy method and type style. The interaction indicates that some type styles are better read when the copy is made by one means rather than another. Error rates varied between less than one percent and more than twenty percent. In general, the user will find that high quality printed materials will be read with a relatively high level of accuracy, but as the quality of the material decreases, the number of errors made by the machine also increases. As this error rate increases, the user will find it increasingly difficult to understand the spoken output.

scholarly journals Radiocarbon Database: A Pilot Project

Radiocarbon ◽  
1983 ◽  
Vol 25 (2) ◽  
pp. 661-666 ◽  
Author(s):  
Steinar Gulliksen
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Retrieval System ◽  
Data Entry ◽  
Pilot Project ◽  
Text Retrieval ◽  
Data Input ◽  
Large Sets ◽  
Optical Character ◽  
Computer Storage

Computer storage and surveys of large sets of data should be an attractive technique for users of 14C dates. Our pilot project demonstrates the effectiveness of a text retrieval system, NOVA STATUS. A small database comprising ca 100 dates, selected from results of the Trondheim 14C laboratory, is generated. Data entry to the computer is made by feeding typewritten forms through a document reader capable of optical character recognition. A text retrieval system allows data input to be in a flexible format. Program systems for text retrieval are in common use and easily implemented for a 14C database.

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 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.

scholarly journals HIGH-TECH START-UPS IN JAPAN: COGENT LABS, AI-OCR SOLUTIONS FOR AUTOMATED BUSINESS PROCESS OUTSOURCING

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Jorge Calvo
Keyword(s):  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Business Processes ◽  
Handwriting Recognition ◽  
Business Case ◽  
High Tech ◽  
Success Factor ◽  
Start Up ◽  
Functional Product

This business research case introduces Cogent Labs, a Japanese high-tech start-up that provides AI-driven technologies, is making the critical transition from an entrepreneur-driven to a mature management-run organization, the company’s business context and technology development. That requires to harmonize the entrepreneurial and managerial capacity, by a collaborative approach integrating cross-functional product teams. The high-tech start-up has demonstrated ability to overcome the transitional stage of the first entrepreneurship to stability and sustainability through the management, while at the same time keeping innovation by adding Natural Language Processing and Times-Series developments, and creativity; rapidly developing new products. The business case demonstrates that in the start-up to managerial transition of a high-tech start-up the key success factor lies in the motivation and coordination of the different professional cultures –scientific and engineering- that should collaborate in the AI research and fast development of viable products. The method is based on interviews conducted with key executives and a strategic analysis of the firm and its rapidly evolving context in terms of artificial intelligence (AI) and deep learning. The start-up company develops AI-based applications like Tegaki AI, supporting their initial clients from the financial sector in the incremental automation of business processes, based on AI- and Internet of Things (IoT)-driven business processes. Tegaki AI triggers non-strategic business decisions through optical character recognition (OCR) and optical handwriting recognition (OHR) algorithms that show 99.2% accuracy. This business case describes the context of entrepreneurship ecosystems in Japan and the economic emergence of business smartization solutions through the new AI paradigm and OHR.

Following the script: Optical Character Recognition Technology and the British Town and Trade Directory

1997 ◽  
Vol 9 (1-3) ◽  
pp. 1-16
Author(s):  
Tim Coles ◽  
Andrew Alexander ◽  
Gareth Shaw
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Data Entry ◽  
Historical Research ◽  
Optical Character ◽  
Data Source ◽  
Series Of Experiments

Directories are a universal data source widely used in urban historical research. This paper reports on a series of experiments to explore the applicability of Optical Character Recognition (OCR) technology as a means of mass directory data entry.

Handwritten Digit Recognition Using Deep Learning

2021 ◽  
pp. 153-158
Author(s):  
Bhagyashree P M ◽  
L K Likhitha ◽  
D S Rajesh
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Handwriting Recognition ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Overall Performance ◽  
Similar Accuracy

Traditional systems of handwritten Digit Recognition have depended on handcrafted functions and a massive amount of previous knowledge. Training an Optical character recognition (OCR) system primarily based totally on those stipulations is a hard task. Research in the handwriting recognition subject is centered on deep learning strategies and has accomplished breakthrough overall performance in the previous couple of years. Convolutional neural networks (CNNs) are very powerful in perceiving the structure of handwritten digits in ways that assist in automated extraction of features and make CNN the most appropriate technique for solving handwriting recognition problems. Here, our goal is to attain similar accuracy through the use of a pure CNN structure.CNN structure is proposed to be able to attain accuracy even higher than that of ensemble architectures, alongside decreased operational complexity and price. The proposed method gives 99.87 accuracy for real-world handwritten digit prediction with less than 0.1 % loss on training with 60000 digits while 10000 under validation.

scholarly journals Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3344 ◽  
Author(s):  
Savita Ahlawat ◽  
Amit Choudhary ◽  
Anand Nayyar ◽  
Saurabh Singh ◽  
Byungun Yoon
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Computational Cost ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition. In addition, we aim to evaluate various SGD optimization algorithms in improving the performance of handwritten digit recognition. A network’s recognition accuracy increases by incorporating ensemble architecture. Here, our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy even better than that of ensemble architectures, along with reduced operational complexity and cost. Moreover, we also present an appropriate combination of learning parameters in designing a CNN that leads us to reach a new absolute record in classifying MNIST handwritten digits. We carried out extensive experiments and achieved a recognition accuracy of 99.87% for a MNIST dataset.

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