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Development of a multi-user handwriting recognition system using Tesseract open source OCR engine
The objective of the paper is to recognize handwritten samples of lower ...
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Recognition of Handwritten Roman Script Using Tesseract Open source OCR Engine
In the present work, we have used Tesseract 2.01 open source Optical Cha...
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Development of a Multi-User Recognition Engine for Handwritten Bangla Basic Characters and Digits
The objective of the paper is to recognize handwritten samples of basic ...
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Recognition of handwritten Roman Numerals using Tesseract open source OCR engine
The objective of the paper is to recognize handwritten samples of Roman ...
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Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition
A novel, generic scheme for off-line handwritten English alphabets chara...
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Synthetic data generation for Indic handwritten text recognition
This paper presents a novel approach to generate synthetic dataset for h...
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Determining Points on Handwritten Mathematical Symbols
In a variety of applications, such as handwritten mathematics and diagra...
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Recognition of Handwritten Textual Annotations using Tesseract Open Source OCR Engine for information Just In Time (iJIT)
Objective of the current work is to develop an Optical Character Recognition (OCR) engine for information Just In Time (iJIT) system that can be used for recognition of handwritten textual annotations of lower case Roman script. Tesseract open source OCR engine under Apache License 2.0 is used to develop user-specific handwriting recognition models, viz., the language sets, for the said system, where each user is identified by a unique identification tag associated with the digital pen. To generate the language set for any user, Tesseract is trained with labeled handwritten data samples of isolated and free-flow texts of Roman script, collected exclusively from that user. The designed system is tested on five different language sets with free- flow handwritten annotations as test samples. The system could successfully segment and subsequently recognize 87.92 handwritten characters in the test samples of five different users.
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