6,774 research outputs found
Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues
Recognizing scene text is a challenging problem, even more so than the
recognition of scanned documents. This problem has gained significant attention
from the computer vision community in recent years, and several methods based
on energy minimization frameworks and deep learning approaches have been
proposed. In this work, we focus on the energy minimization framework and
propose a model that exploits both bottom-up and top-down cues for recognizing
cropped words extracted from street images. The bottom-up cues are derived from
individual character detections from an image. We build a conditional random
field model on these detections to jointly model the strength of the detections
and the interactions between them. These interactions are top-down cues
obtained from a lexicon-based prior, i.e., language statistics. The optimal
word represented by the text image is obtained by minimizing the energy
function corresponding to the random field model. We evaluate our proposed
algorithm extensively on a number of cropped scene text benchmark datasets,
namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word,
and show better performance than comparable methods. We perform a rigorous
analysis of all the steps in our approach and analyze the results. We also show
that state-of-the-art convolutional neural network features can be integrated
in our framework to further improve the recognition performance
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models
Deep learning has shown state-of-art classification performance on datasets
such as ImageNet, which contain a single object in each image. However,
multi-object classification is far more challenging. We present a unified
framework which leverages the strengths of multiple machine learning methods,
viz deep learning, probabilistic models and kernel methods to obtain
state-of-art performance on Microsoft COCO, consisting of non-iconic images. We
incorporate contextual information in natural images through a conditional
latent tree probabilistic model (CLTM), where the object co-occurrences are
conditioned on the extracted fc7 features from pre-trained Imagenet CNN as
input. We learn the CLTM tree structure using conditional pairwise
probabilities for object co-occurrences, estimated through kernel methods, and
we learn its node and edge potentials by training a new 3-layer neural network,
which takes fc7 features as input. Object classification is carried out via
inference on the learnt conditional tree model, and we obtain significant gain
in precision-recall and F-measures on MS-COCO, especially for difficult object
categories. Moreover, the latent variables in the CLTM capture scene
information: the images with top activations for a latent node have common
themes such as being a grasslands or a food scene, and on on. In addition, we
show that a simple k-means clustering of the inferred latent nodes alone
significantly improves scene classification performance on the MIT-Indoor
dataset, without the need for any retraining, and without using scene labels
during training. Thus, we present a unified framework for multi-object
classification and unsupervised scene understanding
WordFences: Text localization and recognition
En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)In recent years, text recognition has achieved remarkable success in recognizing scanned
document text. However, word recognition in natural images is still an open problem,
which generally requires time consuming post-processing steps. We present a novel architecture
for individual word detection in scene images based on semantic segmentation.
Our contributions are twofold: the concept of WordFence, which detects border areas
surrounding each individual word and a unique pixelwise weighted softmax loss function
which penalizes background and emphasizes small text regions. WordFence ensures that
each word is detected individually, and the new loss function provides a strong training
signal to both text and word border localization. The proposed technique avoids intensive
post-processing by combining semantic word segmentation with a voting scheme
for merging segmentations of multiple scales, producing an end-to-end word detection
system. We achieve superior localization recall on common benchmark datasets - 92%
recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, end-to-end
word recognition achieves state-of-the-art 86% F-Score on ICDAR13
Conditional Image-Text Embedding Networks
This paper presents an approach for grounding phrases in images which jointly
learns multiple text-conditioned embeddings in a single end-to-end model. In
order to differentiate text phrases into semantically distinct subspaces, we
propose a concept weight branch that automatically assigns phrases to
embeddings, whereas prior works predefine such assignments. Our proposed
solution simplifies the representation requirements for individual embeddings
and allows the underrepresented concepts to take advantage of the shared
representations before feeding them into concept-specific layers. Comprehensive
experiments verify the effectiveness of our approach across three phrase
grounding datasets, Flickr30K Entities, ReferIt Game, and Visual Genome, where
we obtain a (resp.) 4%, 3%, and 4% improvement in grounding performance over a
strong region-phrase embedding baseline.Comment: ECCV 2018 accepted pape
A fine-grained approach to scene text script identification
This paper focuses on the problem of script identification in unconstrained
scenarios. Script identification is an important prerequisite to recognition,
and an indispensable condition for automatic text understanding systems
designed for multi-language environments. Although widely studied for document
images and handwritten documents, it remains an almost unexplored territory for
scene text images.
We detail a novel method for script identification in natural images that
combines convolutional features and the Naive-Bayes Nearest Neighbor
classifier. The proposed framework efficiently exploits the discriminative
power of small stroke-parts, in a fine-grained classification framework.
In addition, we propose a new public benchmark dataset for the evaluation of
joint text detection and script identification in natural scenes. Experiments
done in this new dataset demonstrate that the proposed method yields state of
the art results, while it generalizes well to different datasets and variable
number of scripts. The evidence provided shows that multi-lingual scene text
recognition in the wild is a viable proposition. Source code of the proposed
method is made available online
- …