2,238 research outputs found
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model
When extracting information from handwritten documents, text transcription
and named entity recognition are usually faced as separate subsequent tasks.
This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks
jointly, using a single neural network with a common architecture used for
plain text recognition. Experimentally, the work has been tested on a
collection of historical marriage records. Results of experiments are presented
to show the effect on the performance for different configurations: different
ways of encoding the information, doing or not transfer learning and processing
at text line or multi-line region level. The results are comparable to state of
the art reported in the ICDAR 2017 Information Extraction competition, even
though the proposed technique does not use any dictionaries, language modeling
or post processing.Comment: To appear in IAPR International Workshop on Document Analysis Systems
2018 (DAS 2018
Key-value information extraction from full handwritten pages
We propose a Transformer-based approach for information extraction from
digitized handwritten documents. Our approach combines, in a single model, the
different steps that were so far performed by separate models: feature
extraction, handwriting recognition and named entity recognition. We compare
this integrated approach with traditional two-stage methods that perform
handwriting recognition before named entity recognition, and present results at
different levels: line, paragraph, and page. Our experiments show that
attention-based models are especially interesting when applied on full pages,
as they do not require any prior segmentation step. Finally, we show that they
are able to learn from key-value annotations: a list of important words with
their corresponding named entities. We compare our models to state-of-the-art
methods on three public databases (IAM, ESPOSALLES, and POPP) and outperform
previous performances on all three datasets
DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
Unconstrained handwritten text recognition is a challenging computer vision
task. It is traditionally handled by a two-step approach, combining line
segmentation followed by text line recognition. For the first time, we propose
an end-to-end segmentation-free architecture for the task of handwritten
document recognition: the Document Attention Network. In addition to text
recognition, the model is trained to label text parts using begin and end tags
in an XML-like fashion. This model is made up of an FCN encoder for feature
extraction and a stack of transformer decoder layers for a recurrent
token-by-token prediction process. It takes whole text documents as input and
sequentially outputs characters, as well as logical layout tokens. Contrary to
the existing segmentation-based approaches, the model is trained without using
any segmentation label. We achieve competitive results on the READ 2016 dataset
at page level, as well as double-page level with a CER of 3.43% and 3.70%,
respectively. We also provide results for the RIMES 2009 dataset at page level,
reaching 4.54% of CER.
We provide all source code and pre-trained model weights at
https://github.com/FactoDeepLearning/DAN
Named Entity Recognition in multilingual handwritten texts
[ES] En nuestro trabajo presentamos un único modelo basado en aprendizaje profundo para la transcripción automática y el reconocimiento de entidades nombradas de textos manuscritos. Este modelo aprovecha las capacidades de generalización de sistemas de reconocimiento, combinando redes neuronales artificiales y n-gramas de caracteres. Se discute la evaluación de dicho sistema y, como consecuencia, se propone una nueva medida de evaluación. Con el fin de mejorar los resultados con respecto a dicha métrica, se evalúan diferentes estrategias de corrección de errores.[EN] In our work we present a single Deep Learning based model for the automatic transcription and Named Entity Recognition of handwritten texts. Such model leverages the generalization capabilities of recognition systems, combining Artificial Neural Networks and n-gram character models. The evaluation of said system is discussed and, as a consequence, a new evaluation metric is proposed. As a means to improve the results in regards to such metric, different error correction strategies are assessed.Villanova Aparisi, D. (2021). Named Entity Recognition in multilingual handwritten texts. Universitat Politècnica de València. http://hdl.handle.net/10251/174942TFG
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads
In processing human produced text using natural language processing (NLP)
techniques, two fundamental subtasks that arise are (i) segmentation of the
plain text into meaningful subunits (e.g., entities), and (ii) dependency
parsing, to establish relations between subunits. In this paper, we develop a
relatively simple and effective neural joint model that performs both
segmentation and dependency parsing together, instead of one after the other as
in most state-of-the-art works. We will focus in particular on the real estate
ad setting, aiming to convert an ad to a structured description, which we name
property tree, comprising the tasks of (1) identifying important entities of a
property (e.g., rooms) from classifieds and (2) structuring them into a tree
format. In this work, we propose a new joint model that is able to tackle the
two tasks simultaneously and construct the property tree by (i) avoiding the
error propagation that would arise from the subtasks one after the other in a
pipelined fashion, and (ii) exploiting the interactions between the subtasks.
For this purpose, we perform an extensive comparative study of the pipeline
methods and the new proposed joint model, reporting an improvement of over
three percentage points in the overall edge F1 score of the property tree.
Also, we propose attention methods, to encourage our model to focus on salient
tokens during the construction of the property tree. Thus we experimentally
demonstrate the usefulness of attentive neural architectures for the proposed
joint model, showcasing a further improvement of two percentage points in edge
F1 score for our application.Comment: Preprint - Accepted for publication in Expert Systems with
Application
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
In this paper we show that reporting a single performance score is
insufficient to compare non-deterministic approaches. We demonstrate for common
sequence tagging tasks that the seed value for the random number generator can
result in statistically significant (p < 10^-4) differences for
state-of-the-art systems. For two recent systems for NER, we observe an
absolute difference of one percentage point F1-score depending on the selected
seed value, making these systems perceived either as state-of-the-art or
mediocre. Instead of publishing and reporting single performance scores, we
propose to compare score distributions based on multiple executions. Based on
the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we
present network architectures that produce both superior performance as well as
are more stable with respect to the remaining hyperparameters.Comment: Accepted at EMNLP 201
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