Recognition and Grouping of Handwritten Text in Diagrams and Equations

2004 ◽  
Author(s):  
M. Shilman ◽  
P. Viola ◽  
K. Chellapilla
Keyword(s):  
Handwritten Text

Poverty, Philanthropy and Social Conditions in Victorian Britain

2020 ◽  
Vol 22 (1) ◽  
pp. 51-55
Author(s):  
Dawn Behrend
Keyword(s):  
Social Conditions ◽  
British Library ◽  
Settlement Houses ◽  
Handwritten Documents ◽  
Victorian Era ◽  
Victorian Britain ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
The Uk ◽  
Keyword Searching

Poverty, Philanthropy and Social Conditions in Victorian Britain published by Adam Matthew Digital is comprised of primary digital materials culled from three major archives in Britain and the UK focused on the experience of poverty in Victorian Britain and efforts involving economic, government, and social reform such as the Poor Law, workhouses, settlement houses, and philanthropic initiatives. Content is derived from the National Archives at Kew, British Library, and Senate House Library and includes pamphlets, correspondence, newspaper clippings, books, and other resources. A small portion of the collection utilizes Adam Matthew Digital’s Handwritten Text Recognition (HTR) to enable keyword searching of handwritten documents. The digitized images and documents are clear, searchable, and user-friendly to access, save, and share. Contract provisions are standard to the product with authenticated access across institutional locations and guidelines for Interlibrary Loan sharing. Pricing is determined by institutional size and enrollment. While the product is a one-time purchase, annual hosting fees apply for ongoing access. Content is currently heavily derived from one archive, the Senate House Library, with pamphlets from this source making up nearly half of the total holdings. Users seeking access to a more extensive collection of similar material may prefer subscribing to JSTOR which includes JSTOR 19th Century British Pamphlets with over 26,000 pamphlets along with secondary scholarly journals and eBooks on the Victorian era. While not providing the primary sources of Poverty, Philanthropy and Social Conditions in Victorian Britain or JSTOR, Historical Abstracts may be an alternative resource in providing access to notable scholarly resources on the period.

HANDWRITTEN WORD RECOGNITION USING CLASSIFIER ENSEMBLES GENERATED FROM MULTIPLE PROTOTYPES

2004 ◽  
Vol 18 (05) ◽  
pp. 957-974 ◽  
Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE
Keyword(s):  
Handwriting Recognition ◽  
Recognition Rate ◽  
Classifier Ensembles ◽  
Multiple Classifier Systems ◽  
Classification Problems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
System 1

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.

INTEGRATION OF n-GRAM LANGUAGE MODELS IN MULTIPLE CLASSIFIER SYSTEMS FOR OFFLINE HANDWRITTEN TEXT LINE RECOGNITION

2008 ◽  
Vol 22 (07) ◽  
pp. 1301-1321 ◽  
Author(s):  
ROMAN BERTOLAMI ◽  
HORST BUNKE
Keyword(s):  
Language Model ◽  
Language Models ◽  
Combination Method ◽  
Text Line ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
N Gram

Current multiple classifier systems for unconstrained handwritten text recognition do not provide a straightforward way to utilize language model information. In this paper, we describe a generic method to integrate a statistical n-gram language model into the combination of multiple offline handwritten text line recognizers. The proposed method first builds a word transition network and then rescores this network with an n-gram language model. Experimental evaluation conducted on a large dataset of offline handwritten text lines shows that the proposed approach improves the recognition accuracy over a reference system as well as over the original combination method that does not include a language model.

Graphic line extraction as an important element of handwriting analysis

2016 ◽  
Vol 293 ◽  
pp. 81-85
Author(s):  
Mieczysław Goc ◽  
◽  
Krystyn Łuszczuk ◽  
Andrzej Łuszczuk ◽  
◽  
...  
Keyword(s):  
Computer Application ◽  
Text Line ◽  
Handwriting Analysis ◽  
Line Extraction ◽  
Handwritten Text

The article presents the capabilities and operating procedures of a computer application EDYTOR, dedicated for easy separation of the handwritten text line from the background containing elements interfering with the examined object. The application, developed by a team of specialists from the Polish Forensic Association, is mainly used in handwriting analysis.

Sign in / Sign up

Export Citation Format

Share Document