Publikation
An Investigative Analysis of Different LSTM Libraries for Supervised and Unsupervised Architectures of OCR Training
Syed Saqib Bukhari; Cannannore Nidhi Narayana Kamath; Sumam Francis; Andreas Dengel
In: ICFHR. International Conference on Frontiers in Handwriting Recognition (ICFHR-2018), The 16th International Conference on Frontiers in Handwriting Recognition, August 5-8, Niagara Falls, USA, IEEE, 2018.
Zusammenfassung
Optical Character Recognition (OCR) involves con-
version of images of text into machine encoded editable text.
Despite the wide research advancements in the field of OCR
systems, the recognition capability of OCR systems on unseen or
degraded historical documents is still questionable. The degra-
dations in the document like torn pages, ink spread and blurred
documents are major challenges especially in the old paper
documents. Most of such degraded documents lack a generalized
and reliable OCR system mainly because of the unavailability
of ground-truth data and poor generalization capabilities of
the OCR systems. Also manually transcribing the documents is
cumbersome task which also require certain language-specific
expertise. This paper presents a feasibility study of different
OCR architectures together with different preprocessing stages
for a reliable OCR on such challenging documents. To this
end, we evaluate various OCR settings on a dataset containing
highly degraded historical German typewriter documents. This
paper investigates various key aspects of OCR training such
as the impact of incorporation of different LSTM libraries,
grayscale or binarized data for training and training data size
used on the subject dataset. In addition, difference in the effect
of using completely manually transcribed data as compared
to semi-corrected ground-truth data for anyOCR architecture
of unsupervised OCR training have been analyzed on a small
dataset. The anyOCR framework has shown promising results
as an efficient OCR system which was evident with its comparison
with other OCR systems. The various factors analyzed provided
a feasible strategy for approaching the problem and evaluating
highly challenging historical documents.