Scale Space Co-Occurrence HOG Features for Word Spotting in Handwritten Document Images

Scale Space Co-Occurrence HOG Features for Word Spotting in Handwritten Document Images

C. Thontadari (Department of Computer Science, Kuvempu University, Shimogga, India) and C. J. Prabhakar (Department of Computer Science, Kuvempu University, Shimogga, India)
Copyright: © 2016 |Pages: 16
DOI: 10.4018/IJCVIP.2016070105
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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.
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1. Introduction

Document Image Analysis (DIA) is one of the active research areas, which attracts research community due to its complexity and the increasing requirement for accessing the content of digitized contents. Optical Character Recognition (OCR) has been investigated for a few decades with huge achievement, which helps to automate the human process. OCR techniques usually recognize words by processing characters independently, works well with machine printed fonts against clean backgrounds. Generally, the large amount of document images is stored in digital libraries, and processing of these documents through the OCR system requires high computation cost because of complexity involved in understanding the page layout of documents, irregular writing styles, faded ink, stained paper and other undesirable factors. In order to overcome these problems, researchers have proposed a technique called word spotting. Word spotting technique is a moderately new alternative for character recognition and retrieval in both printed and handwritten documents.

Handwritten word spotting is the pattern classification task which retrieves words from the document images that are similar to the query word. Generally, a typical word spotting system consists of three main modules: preprocessing, features extraction and feature matching. Among them, features extraction is one of the most important factors for achieving high retrieval performance, because features with strong discriminative information can be well classified even using simplest classifier. The literature survey reveals that Histogram of Oriented Gradients (HOG) descriptor is widely used in several recognition applications because of its discriminating ability compared to other existing feature descriptors. The HOG feature descriptor is developed by Dalal, et al. (2005) for human detection using Support Vector Machine (SVM) classifier. The HOG has been successfully applied in many research fields such as word spotting task (Rodriguez et al., 2008; Terasawa et al., 2009), body parts detection (Corvee et al., 2010), face recognition (Deniz et al., 2011; Shu et al., 2011), character recognition (Newell et al., 2011), text/non-text classification problem (Minetto et al., 2013) and vehicle detection in traffic video (Arrospide et al., 2013).

Rodriguez et al. (2008) have proposed local gradient histogram features for word spotting in unconstrained handwritten documents. A sliding window moves from left to right over a word image. At each position, the window is subdivided into cells, and in each cell, histogram of orientations is accumulated. Slit style HOG features for handwritten document image word spotting is proposed by Terasawa et al. (2009). Newell et al. (2011) have extended the HOG descriptor to include features at multiple scales for character recognition. Saidani et al. (2015) have proposed a novel approach for Arabic and Latin script identification based on Histogram of Oriented Gradients feature descriptors. HOG is first applied at word level based on writing orientation analysis. Then, they are extended to word image partitions to capture fine and discriminating details. The unsupervised segmentation-free HOG based word spotting method was proposed by Almazan, et al. (2014). Documents are represented by a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query.

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