13,599 research outputs found
Machine Learning for handwriting text recognition in historical documents
Olmos
ABSTRACT
In this thesis, we focus on the handwriting text recognition task over historical
documents that are difficult to read for any person that is not an expert in ancient
languages and writing style.
We aim to take advantage and improve the neural networks architectures and
techniques that other authors are proposing for handwriting text recognition in
modern handwritten documents. These models perform this task very precisely
when a large amount of data is available. However, the low availability of labeled
data is a widespread problem in historical documents. The type of writing is
singular, and it is pretty expensive to hire an expert to transcribe a large number
of pages.
After investigating and analyzing the state-of-the-art, we propose the efficient
application of methods such as transfer learning and data augmentation. We also
contribute an algorithm for purging mislabeled samples that affect the learning of
models. Finally, we develop a variational auto encoder method for generating
synthetic samples of handwritten text images for data augmentation.
Experiments are performed on various historical handwritten text databases to
validate the performance of the proposed algorithms. The various included
analyses focus on the evolution of the character and word error rate (CER and
WER) as we increase the training dataset.
One of the most important results is the participation in a contest for transcription
of historical handwritten text. The organizers provided us with a dataset of
documents to train the model, then just a few labeled pages of 5 new documents
were handled to adjust the solution further. Finally, the transcription of nonlabeled
images was requested to evaluate the algorithm. Our method raked
second in this contest
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Handwritten Text Recognition (HTR) is still a challenging problem because it
must deal with two important difficulties: the variability among writing
styles, and the scarcity of labelled data. To alleviate such problems,
synthetic data generation and data augmentation are typically used to train HTR
systems. However, training with such data produces encouraging but still
inaccurate transcriptions in real words. In this paper, we propose an
unsupervised writer adaptation approach that is able to automatically adjust a
generic handwritten word recognizer, fully trained with synthetic fonts,
towards a new incoming writer. We have experimentally validated our proposal
using five different datasets, covering several challenges (i) the document
source: modern and historic samples, which may involve paper degradation
problems; (ii) different handwriting styles: single and multiple writer
collections; and (iii) language, which involves different character
combinations. Across these challenging collections, we show that our system is
able to maintain its performance, thus, it provides a practical and generic
approach to deal with new document collections without requiring any expensive
and tedious manual annotation step.Comment: Accepted to WACV 202
Boosting Handwriting Text Recognition in Small Databases with Transfer Learning
In this paper we deal with the offline handwriting text recognition (HTR)
problem with reduced training datasets. Recent HTR solutions based on
artificial neural networks exhibit remarkable solutions in referenced
databases. These deep learning neural networks are composed of both
convolutional (CNN) and long short-term memory recurrent units (LSTM). In
addition, connectionist temporal classification (CTC) is the key to avoid
segmentation at character level, greatly facilitating the labeling task. One of
the main drawbacks of the CNNLSTM-CTC (CLC) solutions is that they need a
considerable part of the text to be transcribed for every type of calligraphy,
typically in the order of a few thousands of lines. Furthermore, in some
scenarios the text to transcribe is not that long, e.g. in the Washington
database. The CLC typically overfits for this reduced number of training
samples. Our proposal is based on the transfer learning (TL) from the
parameters learned with a bigger database. We first investigate, for a reduced
and fixed number of training samples, 350 lines, how the learning from a large
database, the IAM, can be transferred to the learning of the CLC of a reduced
database, Washington. We focus on which layers of the network could be not
re-trained. We conclude that the best solution is to re-train the whole CLC
parameters initialized to the values obtained after the training of the CLC
from the larger database. We also investigate results when the training size is
further reduced. The differences in the CER are more remarkable when training
with just 350 lines, a CER of 3.3% is achieved with TL while we have a CER of
18.2% when training from scratch. As a byproduct, the learning times are quite
reduced. Similar good results are obtained from the Parzival database when
trained with this reduced number of lines and this new approach.Comment: ICFHR 2018 Conferenc
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis
Automatic analysis of scanned historical documents comprises a wide range of
image analysis tasks, which are often challenging for machine learning due to a
lack of human-annotated learning samples. With the advent of deep neural
networks, a promising way to cope with the lack of training data is to
pre-train models on images from a different domain and then fine-tune them on
historical documents. In the current research, a typical example of such
cross-domain transfer learning is the use of neural networks that have been
pre-trained on the ImageNet database for object recognition. It remains a
mostly open question whether or not this pre-training helps to analyse
historical documents, which have fundamentally different image properties when
compared with ImageNet. In this paper, we present a comprehensive empirical
survey on the effect of ImageNet pre-training for diverse historical document
analysis tasks, including character recognition, style classification,
manuscript dating, semantic segmentation, and content-based retrieval. While we
obtain mixed results for semantic segmentation at pixel-level, we observe a
clear trend across different network architectures that ImageNet pre-training
has a positive effect on classification as well as content-based retrieval
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
There are two types of information in each handwritten word image: explicit
information which can be easily read or derived directly, such as lexical
content or word length, and implicit attributes such as the author's identity.
Whether features learned by a neural network for one task can be used for
another task remains an open question. In this paper, we present a deep
adaptive learning method for writer identification based on single-word images
using multi-task learning. An auxiliary task is added to the training process
to enforce the emergence of reusable features. Our proposed method transfers
the benefits of the learned features of a convolutional neural network from an
auxiliary task such as explicit content recognition to the main task of writer
identification in a single procedure. Specifically, we propose a new adaptive
convolutional layer to exploit the learned deep features. A multi-task neural
network with one or several adaptive convolutional layers is trained
end-to-end, to exploit robust generic features for a specific main task, i.e.,
writer identification. Three auxiliary tasks, corresponding to three explicit
attributes of handwritten word images (lexical content, word length and
character attributes), are evaluated. Experimental results on two benchmark
datasets show that the proposed deep adaptive learning method can improve the
performance of writer identification based on single-word images, compared to
non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio
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