In this paper, the authors proposed a Scale Space Co-occurrence Histograms of Oriented Gradients method (SS Co-HOG) for retrieving words from digitized handwritten documents. The poor performance of HOG based word spotting in handwritten documents is due to that HOG ignores spatial information of neighboring pixels whereas Co-HOG captures the spatial information of neighboring pixels through counting the occurrence of the gradient orientations of two or more neighboring pixels. The authors employed three scale parameter representation of an image and at each scale, they divide the word image into blocks and Co-HOG features are extracted from each block and finally concatenate them into form a feature descriptor. The proposed method is evaluated using precision and recall metrics through experimentation conducted on popular datasets such as IAM and GW and confirmed that their method outperforms for both the datasets.
Abstract-In this paper, we present a segmentation based word spotting method for handwritten document images using Co-occurrence Histograms of Oriented Gradients (Co-HOG) descriptor. The drawback of Histogram of Oriented Gradients (HOG) is that HOG ignores spatial information of adjacent pixels where as the Co-HOG take into account spatial contextual information by capturing the co-occurrence of orientation pairs of neighbouring pixels. In order to construct Co-HOG descriptor for word spotting, we divide a word image into blocks and Co-HOG features are extracted from each block and finally concatenate them to form a feature descriptor. The proposed method is evaluated using precision and recall metrics through experimentation conducted on popular GW dataset and confirmed that our method outperforms for this dataset.Keywords-Word spotting, Character Recognition, George Washington, Dynamic Time Warping, Hidden Markov Models I. INTRODUCTIONRecently Document Image Analysis is become one of dynamic research field which draws an attention of researcher due to its complexity and growing requirement for accessing the content of digitized information. Optical Character Recognition (OCR) has been explored for a few decades with massive accomplishment which facilitates to automate human procedure. OCR techniques usually recognize words by processing fonts independently and works well with machine printed fonts against clean environment. Generally, big quantity of document images are accumulated in digital libraries, and processing of these documents with the help of OCR requires high computation rate due to difficulty involved in understanding the page layout of digitized documents, irregular writing manner, dull ink, stained paper and other adverse factors. In order to overcome these problems, researchers have proposed a method called word spotting. Word spotting method is a moderately new alternative for text recognition and retrieval in digitized printed and handwritten documents.Handwritten word spotting is the pattern classification mission which consists of detecting given query word in handwritten document images. The word spotting in handwritten documents is not completely solved due to various challenges posed by handwritten documents. Hence, we focused on word spotting in handwritten documents rather than printed documents. Generally, a word spotting method consists of three main modules: pre-processing, feature extraction and feature matching. Among them, feature extraction is one of most important factors for achieving high retrieval performance, because of feature with strong discriminative information can be well classified even using with simplest classifier.The literature investigation exposes that HOG descriptor is extensively used in numerous recognition applications because of its discriminative capability compared to other existing feature descriptors. The HOG descriptor is developed by Dalal Importantly, HOG considers orientation of only isolated pixels, whereas spatial information of adjacent pixels i...
In this paper, we present a segmentation-based word spotting method for handwritten documents using Bag of Visual Words (BoVW)
In this chapter, the authors present a segmentation-based word spotting method for handwritten documents using bag of visual words (BoVW) framework based on co-occurrence histograms of oriented gradients (Co-HOG) features. The Co-HOG descriptor captures the word image shape information and encodes the local spatial information by counting the co-occurrence of gradient orientation of neighbor pixel pairs. The handwritten document images are segmented into words and each word image is represented by a vector that contains the frequency of visual words appeared in the image. In order to include spatial information to the BoVW framework, the authors adopted spatial pyramid matching (SPM) method. The proposed method is evaluated using precision and recall metrics through experimentation conducted on popular datasets such as GW and IAM. The performance analysis confirmed that the method outperforms existing word spotting techniques.
In this article, the authors propose a segmentation-free word spotting in handwritten document images using a Bag of Visual Words (BoVW) framework based on the co-occurrence histogram of oriented gradient (Co-HOG) descriptor. Initially, the handwritten document is represented using visual word vectors which are obtained based on the frequency of occurrence of Co-HOG descriptor within local patches of the document. The visual word representation vector does not consider their spatial location and spatial information helps to determine a location exclusively with visual information when the different location can be perceived as the same. Hence, to add spatial distribution information of visual words into the unstructured BoVW framework, the authors adopted spatial pyramid matching (SPM) technique. The performance of the proposed method evaluated using popular datasets and it is confirmed that the authors' method outperforms existing segmentation free word spotting techniques.
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