18,956 research outputs found
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
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
Deep Interactive Region Segmentation and Captioning
With recent innovations in dense image captioning, it is now possible to
describe every object of the scene with a caption while objects are determined
by bounding boxes. However, interpretation of such an output is not trivial due
to the existence of many overlapping bounding boxes. Furthermore, in current
captioning frameworks, the user is not able to involve personal preferences to
exclude out of interest areas. In this paper, we propose a novel hybrid deep
learning architecture for interactive region segmentation and captioning where
the user is able to specify an arbitrary region of the image that should be
processed. To this end, a dedicated Fully Convolutional Network (FCN) named
Lyncean FCN (LFCN) is trained using our special training data to isolate the
User Intention Region (UIR) as the output of an efficient segmentation. In
parallel, a dense image captioning model is utilized to provide a wide variety
of captions for that region. Then, the UIR will be explained with the caption
of the best match bounding box. To the best of our knowledge, this is the first
work that provides such a comprehensive output. Our experiments show the
superiority of the proposed approach over state-of-the-art interactive
segmentation methods on several well-known datasets. In addition, replacement
of the bounding boxes with the result of the interactive segmentation leads to
a better understanding of the dense image captioning output as well as accuracy
enhancement for the object detection in terms of Intersection over Union (IoU).Comment: 17, pages, 9 figure
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