4,709 research outputs found
Logical segmentation for article extraction in digitized old newspapers
Newspapers are documents made of news item and informative articles. They are
not meant to be red iteratively: the reader can pick his items in any order he
fancies. Ignoring this structural property, most digitized newspaper archives
only offer access by issue or at best by page to their content. We have built a
digitization workflow that automatically extracts newspaper articles from
images, which allows indexing and retrieval of information at the article
level. Our back-end system extracts the logical structure of the page to
produce the informative units: the articles. Each image is labelled at the
pixel level, through a machine learning based method, then the page logical
structure is constructed up from there by the detection of structuring entities
such as horizontal and vertical separators, titles and text lines. This logical
structure is stored in a METS wrapper associated to the ALTO file produced by
the system including the OCRed text. Our front-end system provides a web high
definition visualisation of images, textual indexing and retrieval facilities,
searching and reading at the article level. Articles transcriptions can be
collaboratively corrected, which as a consequence allows for better indexing.
We are currently testing our system on the archives of the Journal de Rouen,
one of France eldest local newspaper. These 250 years of publication amount to
300 000 pages of very variable image quality and layout complexity. Test year
1808 can be consulted at plair.univ-rouen.fr.Comment: ACM Document Engineering, France (2012
Word Recognition with Deep Conditional Random Fields
Recognition of handwritten words continues to be an important problem in
document analysis and recognition. Existing approaches extract hand-engineered
features from word images--which can perform poorly with new data sets.
Recently, deep learning has attracted great attention because of the ability to
learn features from raw data. Moreover they have yielded state-of-the-art
results in classification tasks including character recognition and scene
recognition. On the other hand, word recognition is a sequential problem where
we need to model the correlation between characters. In this paper, we propose
using deep Conditional Random Fields (deep CRFs) for word recognition.
Basically, we combine CRFs with deep learning, in which deep features are
learned and sequences are labeled in a unified framework. We pre-train the deep
structure with stacked restricted Boltzmann machines (RBMs) for feature
learning and optimize the entire network with an online learning algorithm. The
proposed model was evaluated on two datasets, and seen to perform significantly
better than competitive baseline models. The source code is available at
https://github.com/ganggit/deepCRFs.Comment: 5 pages, published in ICIP 2016. arXiv admin note: substantial text
overlap with arXiv:1412.339
Handwriting Recognition of Historical Documents with few labeled data
Historical documents present many challenges for offline handwriting
recognition systems, among them, the segmentation and labeling steps. Carefully
annotated textlines are needed to train an HTR system. In some scenarios,
transcripts are only available at the paragraph level with no text-line
information. In this work, we demonstrate how to train an HTR system with few
labeled data. Specifically, we train a deep convolutional recurrent neural
network (CRNN) system on only 10% of manually labeled text-line data from a
dataset and propose an incremental training procedure that covers the rest of
the data. Performance is further increased by augmenting the training set with
specially crafted multiscale data. We also propose a model-based normalization
scheme which considers the variability in the writing scale at the recognition
phase. We apply this approach to the publicly available READ dataset. Our
system achieved the second best result during the ICDAR2017 competition
- …