85 research outputs found
Transfer Learning for OCRopus Model Training on Early Printed Books
A method is presented that significantly reduces the character error rates
for OCR text obtained from OCRopus models trained on early printed books when
only small amounts of diplomatic transcriptions are available. This is achieved
by building from already existing models during training instead of starting
from scratch. To overcome the discrepancies between the set of characters of
the pretrained model and the additional ground truth the OCRopus code is
adapted to allow for alphabet expansion or reduction. The character set is now
capable of flexibly adding and deleting characters from the pretrained alphabet
when an existing model is loaded. For our experiments we use a self-trained
mixed model on early Latin prints and the two standard OCRopus models on modern
English and German Fraktur texts. The evaluation on seven early printed books
showed that training from the Latin mixed model reduces the average amount of
errors by 43% and 26%, respectively compared to training from scratch with 60
and 150 lines of ground truth, respectively. Furthermore, it is shown that even
building from mixed models trained on data unrelated to the newly added
training and test data can lead to significantly improved recognition results
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Deep LSTM is an ideal candidate for text recognition. However text
recognition involves some initial image processing steps like segmentation of
lines and words which can induce error to the recognition system. Without
segmentation, learning very long range context is difficult and becomes
computationally intractable. Therefore, alternative soft decisions are needed
at the pre-processing level. This paper proposes a hybrid text recognizer using
a deep recurrent neural network with multiple layers of abstraction and long
range context along with a language model to verify the performance of the deep
neural network. In this paper we construct a multi-hypotheses tree architecture
with candidate segments of line sequences from different segmentation
algorithms at its different branches. The deep neural network is trained on
perfectly segmented data and tests each of the candidate segments, generating
unicode sequences. In the verification step, these unicode sequences are
validated using a sub-string match with the language model and best first
search is used to find the best possible combination of alternative hypothesis
from the tree structure. Thus the verification framework using language models
eliminates wrong segmentation outputs and filters recognition errors
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 century
Fraktur scripts without book-specific training using mixed models, i.e. models
trained to recognize a variety of fonts and typesets from previously unseen
sources. We describe the training process leading to strong mixed OCR models
and compare them to freely available models of the popular open source engines
OCRopus and Tesseract as well as the commercial state of the art system ABBYY.
For evaluation, we use a varied collection of unseen data from books, journals,
and a dictionary from the 19th century. The experiments show that training
mixed models with real data is superior to training with synthetic data and
that the novel OCR engine Calamari outperforms the other engines considerably,
on average reducing ABBYYs character error rate (CER) by over 70%, resulting in
an average CER below 1%.Comment: Submitted to DHd 2019 (https://dhd2019.org/) which demands a...
creative... submission format. Consequently, some captions might look weird
and some links aren't clickable. Extended version with more technical details
and some fixes to follo
Implicit Language Model in LSTM for OCR
Neural networks have become the technique of choice for OCR, but many aspects
of how and why they deliver superior performance are still unknown. One key
difference between current neural network techniques using LSTMs and the
previous state-of-the-art HMM systems is that HMM systems have a strong
independence assumption. In comparison LSTMs have no explicit constraints on
the amount of context that can be considered during decoding. In this paper we
show that they learn an implicit LM and attempt to characterize the strength of
the LM in terms of equivalent n-gram context. We show that this implicitly
learned language model provides a 2.4\% CER improvement on our synthetic test
set when compared against a test set of random characters (i.e. not naturally
occurring sequences), and that the LSTM learns to use up to 5 characters of
context (which is roughly 88 frames in our configuration). We believe that this
is the first ever attempt at characterizing the strength of the implicit LM in
LSTM based OCR systems
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