137 research outputs found
Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding
Retrieval of text information from natural scene images and video frames is a
challenging task due to its inherent problems like complex character shapes,
low resolution, background noise, etc. Available OCR systems often fail to
retrieve such information in scene/video frames. Keyword spotting, an
alternative way to retrieve information, performs efficient text searching in
such scenarios. However, current word spotting techniques in scene/video images
are script-specific and they are mainly developed for Latin script. This paper
presents a novel word spotting framework using dynamic shape coding for text
retrieval in natural scene image and video frames. The framework is designed to
search query keyword from multiple scripts with the help of on-the-fly
script-wise keyword generation for the corresponding script. We have used a
two-stage word spotting approach using Hidden Markov Model (HMM) to detect the
translated keyword in a given text line by identifying the script of the line.
A novel unsupervised dynamic shape coding based scheme has been used to group
similar shape characters to avoid confusion and to improve text alignment.
Next, the hypotheses locations are verified to improve retrieval performance.
To evaluate the proposed system for searching keyword from natural scene image
and video frames, we have considered two popular Indic scripts such as Bangla
(Bengali) and Devanagari along with English. Inspired by the zone-wise
recognition approach in Indic scripts[1], zone-wise text information has been
used to improve the traditional word spotting performance in Indic scripts. For
our experiment, a dataset consisting of images of different scenes and video
frames of English, Bangla and Devanagari scripts were considered. The results
obtained showed the effectiveness of our proposed word spotting approach.Comment: Multimedia Tools and Applications, Springe
Keyword spotting for cursive document retrieval
We present one of the first attempts towards automatic retrieval of documents, in the noisy environment of unconstrained, multiple author handwritten forms. The documents were written in cursive script for which conventional OCR and text retrieval engines are not adequate. We focus on a visual word spotting indexing scheme for scanned documents housed in the Archives of the Indies in Seville, Spain. The framework presented utilizes pattern recognition, learning and information fusion methods, and is motivated from human word-spotting studies. The proposed system is described and initial results are presented
Keyword spotting for cursive document retrieval
We present one of the first attempts towards automatic retrieval of documents, in the noisy environment of unconstrained, multiple author handwritten forms. The documents were written in cursive script for which conventional OCR and text retrieval engines are not adequate. We focus on a visual word spotting indexing scheme for scanned documents housed in the Archives of the Indies in Seville, Spain. The framework presented utilizes pattern recognition, learning and information fusion methods, and is motivated from human word-spotting studies. The proposed system is described and initial results are presented
HMM word graph based keyword spotting in handwritten document images
[EN] Line-level keyword spotting (KWS) is presented on the basis of frame-level word posterior
probabilities. These posteriors are obtained using word graphs derived from the recogni-
tion process of a full-fledged handwritten text recognizer based on hidden Markov models
and N-gram language models. This approach has several advantages. First, since it uses
a holistic, segmentation-free technology, it does not require any kind of word or charac-
ter segmentation. Second, the use of language models allows the context of each spotted
word to be taken into account, thereby considerably increasing KWS accuracy. And third,
the proposed KWS scores are based on true posterior probabilities, taking into account
all (or most) possible word segmentations of the input image. These scores are properly
bounded and normalized. This mathematically clean formulation lends itself to smooth,
threshold-based keyword queries which, in turn, permit comfortable trade-offs between
search precision and recall. Experiments are carried out on several historic collections of
handwritten text images, as well as a well-known data set of modern English handwrit-
ten text. According to the empirical results, the proposed approach achieves KWS results
comparable to those obtained with the recently-introduced "BLSTM neural networks KWS"
approach and clearly outperform the popular, state-of-the-art "Filler HMM" KWS method.
Overall, the results clearly support all the above-claimed advantages of the proposed ap-
proach.This work has been partially supported by the Generalitat Valenciana under the Prometeo/2009/014 project grant ALMA-MATER, and through the EU projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon 2020 programme, grant Ref. 674943).Toselli, AH.; Vidal, E.; Romero, V.; Frinken, V. (2016). HMM word graph based keyword spotting in handwritten document images. Information Sciences. 370:497-518. https://doi.org/10.1016/j.ins.2016.07.063S49751837
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