3,975 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
AON: Towards Arbitrarily-Oriented Text Recognition
Recognizing text from natural images is a hot research topic in computer
vision due to its various applications. Despite the enduring research of
several decades on optical character recognition (OCR), recognizing texts from
natural images is still a challenging task. This is because scene texts are
often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted)
arrangements, which have not yet been well addressed in the literature.
Existing methods on text recognition mainly work with regular (horizontal and
frontal) texts and cannot be trivially generalized to handle irregular texts.
In this paper, we develop the arbitrary orientation network (AON) to directly
capture the deep features of irregular texts, which are combined into an
attention-based decoder to generate character sequence. The whole network can
be trained end-to-end by using only images and word-level annotations.
Extensive experiments on various benchmarks, including the CUTE80,
SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed
AON-based method achieves the-state-of-the-art performance in irregular
datasets, and is comparable to major existing methods in regular datasets.Comment: Accepted by CVPR201
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