Multiple Classifier Methods for Offline Handwritten Text Line Recognition

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.

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HANDWRITTEN WORD RECOGNITION USING CLASSIFIER ENSEMBLES GENERATED FROM MULTIPLE PROTOTYPES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001404003496 ◽

2004 ◽

Vol 18 (05) ◽

pp. 957-974 ◽

Cited By ~ 4

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.

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Graphic line extraction as an important element of handwriting analysis

Issues of Forensic Science ◽

10.34836/pk.2016.293.4 ◽

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.

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Unconstrained Handwritten Text Line Segmentation for Kannada Language

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9624.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 953-956

Keyword(s):

Character Recognition ◽

Recognition System ◽

Text Line ◽

Connected Component ◽

Horizontal Projection ◽

Text Documents ◽

Handwritten Text ◽

Kannada Language ◽

System Separation ◽

Line Segmentation

Segmentation is division of something into smaller parts and one of the Component of character recognition system. Separation of characters, words and lines are done in Segmentation from text documents. character recognition is a process which allows computers to recognize written or printed characters such as numbers or letters and to change them into a form that the computer can use. the accuracy of OCR system is done by taking the output of an OCR run for an image and comparing it to the original version of the same text. The main aim of this paper is to find out the various text line segmentations are Projection profiles, Weighted Bucket Method. Proposed method is horizontal projection profile and connected component method on Handwritten Kannada language. These methods are used for experimentation and finally comparing their accuracy and results.

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