  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%, 81.53% and 65.71% respectively. The overall character-level accuracy
of the system is observed as 78.39%. The system fails to segment 10.96%
characters and erroneously classifies 10.65% characters on the overall dataset.
