-
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 ...
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 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 Roman Numerals using Tesseract open source OCR engine
The objective of the paper is to recognize handwritten samples of Roman ...
read it
-
Realistic Physics Based Character Controller
Over the course of the last several years there was a strong interest in...
read it
-
Full Page Handwriting Recognition via Image to Sequence Extraction
We present a Neural Network based Handwritten Text Recognition (HTR) mod...
read it
-
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text
Handling large corpuses of documents is of significant importance in man...
read it
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 case Roman script using Tesseract open source Optical Character Recognition (OCR) engine under Apache License 2.0. Handwritten data samples containing isolated and free-flow text were collected from different users. Tesseract is trained with user-specific data samples of both the categories of document pages to generate separate user-models representing a unique language-set. Each such language-set recognizes isolated and free-flow handwritten test samples collected from the designated user. On a three user model, the system is trained with 1844, 1535 and 1113 isolated handwritten character samples collected from three different users and the performance is tested on 1133, 1186 and 1204 character samples, collected form the test sets of the three users respectively. The user specific character level accuracies were obtained as 87.92 of the system is observed as 78.39 characters and erroneously classifies 10.65
READ FULL TEXT
Comments
There are no comments yet.