-
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 ...
read it
-
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...
read it
-
Recognition of handwritten Roman Numerals using Tesseract open source OCR engine
The objective of the paper is to recognize handwritten samples of Roman ...
read it
-
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...
read it
-
Recognition of Handwritten Bangla Basic Characters and Digits using Convex Hull based Feature Set
In dealing with the problem of recognition of handwritten character patt...
read it
-
Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier
A novel approach for recognition of handwritten compound Bangla characte...
read it
-
Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition
In handwritten character recognition, benchmark database plays an import...
read it
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 Bangla characters using Tesseract open source Optical Character Recognition (OCR) engine under Apache License 2.0. Handwritten data samples containing isolated Bangla basic characters and digits were collected from different users. Tesseract is trained with user-specific data samples of document pages to generate separate user-models representing a unique language-set. Each such language-set recognizes isolated basic Bangla handwritten test samples collected from the designated users. On a three user model, the system is trained with 919, 928 and 648 isolated handwritten character and digit samples and the performance is tested on 1527, 14116 and 1279 character and digit samples, collected form the test datasets of the three users respectively. The user specific character/digit recognition accuracies were obtained as 90.66 91.66 level accuracy of the system is observed as 92.15 to segment 12.33 7.85
READ FULL TEXT
Comments
There are no comments yet.