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Deep learning had a significant impact on diverse pattern recognition tasks in the recent past. In this paper, we investigate its potential for keyword spotting in handwritten documents by designing a novel feature extraction system based on Convolutional Deep Belief Networks. Sliding window features are learned from
We propose a new segmentation-free method for keyword spotting in handwritten documents based on Heat Kernel Signature (HKS). After key points are located by the key point detector for SIFT on the document pages and the query image, HKS descriptors are extracted from a local patch centered at each key point. In order
We propose a script independent bayesian framework for keyword spotting in multilingual handwritten documents. The approach relies on local character level score and global word level hypothesis scores and learns a bayesian logistic regression classifier to distinguish between keywords and non-keywords. In a bayesian
We propose the Bayesian Active Learning by Disagreement (BALD) model for keyword spotting in handwritten documents. In the context of keyword spotting in handwritten documents, the background text is all regions in the document that do not contain the keywords. The model tries to learn certain characteristics of the
Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template
Handwritten word spotting aims at making document images amenable to browsing and searching by keyword retrieval. In this paper, we present a word spotting system based on Hidden Markov Models (HMM) that uses trained subword models to spot keywords. With the proposed method, arbitrary keywords can be spotted that do
A hidden-Markov-model (HMM)-based system for font-independent spotting of user-specified keywords in a scanned image is described. Word bounding boxes of potential keywords are extracted from the image using a morphology-based preprocessor. Feature vectors based on the external shape and internal structure of the word
We present a handwritten text Keyword Spotting (KWS) approach based on the combination of KWS methods using word-graphs (WGs) and character-lattices (CLs). It aims to solve the problem that WG-based models present for out of vocabulary (OOV) keywords: since there is no available information about them in the lexicon
segmentation. A keyword is represented by concatenating its character models. We propose and compare two systems: a script identifier based (IDB) and a script identifier free (IDF) system. IDB uses a HMM based script identifier before spotting a keyword. While, IDF does the spotting without the script identification. The system
In this paper we present a line based word spotting system based on Hidden Markov Model for offline Indic scripts such as Bangla (Bengali) and Devanagari. We propose a novel approach of combining foreground and background information of text line images for keyword-spotting by character filler models. The candidate
We propose a novel approach for helping content transcription of handwritten digital documents. The approach adopts a segmentation based keyword retrieval approach that follows query-by-string paradigm and exploits the user validation of the retrieved words to improve its performance during operation. Our approach
-by-example), which can retrieve morphologically similar words that have matching sub-words. Further, to enable query-by-keyword, we build an automated scheme to generate labeled exemplars for characters and character n-grams, from unconstrained handwritten documents. We pose this problem as one of weakly-supervised
images amenable to browsing and searching in digital libraries. In this paper, we propose a novel multi-pass alignment method based on Hidden Markov Models (HMM) that combines text line recognition, string alignment, and keyword spotting to cope with word substitutions, deletions, and insertions in the transcription. In a
Automatic image annotation is a promising key to semantic-based image retrieval by keywords. Most existing automatic image annotation approaches focused on exploring the relationship between images and annotation words and neglected the semantic information of the annotated keywords. In this paper we propose a semi
In this paper, we propose a method for spotting keywords in images of handwritten text. Relying on an object detection system in real images, local contour features are extracted from segmented word images in order to obtain a representative shape of a word-class. Thus, word spotting is cast following a query-by-word
its relevance. During search, we retrieve similar images containing the correct keywords for a given target image. For example, we prioritize images where extracted objects of interest from the target images are dominant as it is more likely that words associated with the images describe the objects. We tailored our
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