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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 Text Image Using Multilayer Perceptron
The biggest challenge in the field of image processing is to recognize d...
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Full Page Handwriting Recognition via Image to Sequence Extraction
We present a Neural Network based Handwritten Text Recognition (HTR) mod...
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State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines
In this paper we evaluate Optical Character Recognition (OCR) of 19th ce...
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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 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 Recogni...
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A Novel Approach to OCR using Image Recognition based Classification for Ancient Tamil Inscriptions in Temples
Recognition of ancient Tamil characters has always been a challenge for ...
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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 Character Recognition (OCR) Engine under Apache License 2.0 for recognition of handwriting samples of lower case Roman script. Handwritten isolated and free-flow text samples were collected from multiple users. Tesseract is trained to recognize user-specific handwriting samples of both the categories of document pages. On a single user model, the system is trained with 1844 isolated handwritten characters and the performance is tested on 1133 characters, taken form the test set. The overall character-level accuracy of the system is observed as 83.5 and erroneously classifies 10.94
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