154 research outputs found
Generating Text Sequence Images for Recognition
Recently, methods based on deep learning have dominated the field of text
recognition. With a large number of training data, most of them can achieve the
state-of-the-art performances. However, it is hard to harvest and label
sufficient text sequence images from the real scenes. To mitigate this issue,
several methods to synthesize text sequence images were proposed, yet they
usually need complicated preceding or follow-up steps. In this work, we present
a method which is able to generate infinite training data without any auxiliary
pre/post-process. We tackle the generation task as an image-to-image
translation one and utilize conditional adversarial networks to produce
realistic text sequence images in the light of the semantic ones. Some
evaluation metrics are involved to assess our method and the results
demonstrate that the caliber of the data is satisfactory. The code and dataset
will be publicly available soon
Visual Semantic Re-ranker for Text Spotting
Many current state-of-the-art methods for text recognition are based on
purely local information and ignore the semantic correlation between text and
its surrounding visual context. In this paper, we propose a post-processing
approach to improve the accuracy of text spotting by using the semantic
relation between the text and the scene. We initially rely on an off-the-shelf
deep neural network that provides a series of text hypotheses for each input
image. These text hypotheses are then re-ranked using the semantic relatedness
with the object in the image. As a result of this combination, the performance
of the original network is boosted with a very low computational cost. The
proposed framework can be used as a drop-in complement for any text-spotting
algorithm that outputs a ranking of word hypotheses. We validate our approach
on ICDAR'17 shared task dataset
Enhanced Characterness for Text Detection in the Wild
Text spotting is an interesting research problem as text may appear at any
random place and may occur in various forms. Moreover, ability to detect text
opens the horizons for improving many advanced computer vision problems. In
this paper, we propose a novel language agnostic text detection method
utilizing edge enhanced Maximally Stable Extremal Regions in natural scenes by
defining strong characterness measures. We show that a simple combination of
characterness cues help in rejecting the non text regions. These regions are
further fine-tuned for rejecting the non-textual neighbor regions.
Comprehensive evaluation of the proposed scheme shows that it provides
comparative to better generalization performance to the traditional methods for
this task
Fused Text Segmentation Networks for Multi-oriented Scene Text Detection
In this paper, we introduce a novel end-end framework for multi-oriented
scene text detection from an instance-aware semantic segmentation perspective.
We present Fused Text Segmentation Networks, which combine multi-level features
during the feature extracting as text instance may rely on finer feature
expression compared to general objects. It detects and segments the text
instance jointly and simultaneously, leveraging merits from both semantic
segmentation task and region proposal based object detection task. Not
involving any extra pipelines, our approach surpasses the current state of the
art on multi-oriented scene text detection benchmarks: ICDAR2015 Incidental
Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever,
we report a baseline on total-text containing curved text which suggests
effectiveness of the proposed approach.Comment: Accepted by ICPR201
Object Proposals for Text Extraction in the Wild
Object Proposals is a recent computer vision technique receiving increasing
interest from the research community. Its main objective is to generate a
relatively small set of bounding box proposals that are most likely to contain
objects of interest. The use of Object Proposals techniques in the scene text
understanding field is innovative. Motivated by the success of powerful while
expensive techniques to recognize words in a holistic way, Object Proposals
techniques emerge as an alternative to the traditional text detectors.
In this paper we study to what extent the existing generic Object Proposals
methods may be useful for scene text understanding. Also, we propose a new
Object Proposals algorithm that is specifically designed for text and compare
it with other generic methods in the state of the art. Experiments show that
our proposal is superior in its ability of producing good quality word
proposals in an efficient way. The source code of our method is made publicly
available.Comment: 13th International Conference on Document Analysis and Recognition
(ICDAR 2015
- …