Journal Article10.1007/S10032-012-0187-7
Word shape descriptor-based document image indexing: a new DBH-based approach
19
TL;DR: The exhaustive experimental evaluation of the proposed framework on a collection of documents belonging to Devanagari, Bengali and English scripts has yielded encouraging results.
read more
Abstract: In this paper, we propose a novel feature representation for binary patterns by exploiting the object shape information. Initial evaluation of the representation is performed for Bengali and Gujarati script character classification. The extension of the representation for word images is presented subsequently. The proposed feature representation in combination with distance-based hashing is applied for defining novel word image-based document image indexing and retrieval framework. The concept of hierarchical hashing is utilized to reduce the retrieval time complexity. In addition, with the objective of reduction in the size of hashing data structure, the concept of multi-probe hashing is extended for binary mapping functions. The exhaustive experimental evaluation of the proposed framework on a collection of documents belonging to Devanagari, Bengali and English scripts has yielded encouraging results.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Unsupervised Word Spotting in Historical Handwritten Document Images Using Document-Oriented Local Features
TL;DR: A new method that permits effective word spotting in handwritten documents is presented that it relies upon document-oriented local features, which take into account information around representative keypoints as well a matching process that incorporates spatial context in a local proximity search without using any training data.
38
Segmentation-Based Historical Handwritten Word Spotting Using Document-Specific Local Features
Konstantinos Zagoris,Ioannis Pratikakis,Basilis Gatos +2 more
- 15 Dec 2014
TL;DR: A new method that permits effective word spotting in handwritten documents is presented that relies upon document-specific local features which take into account texture information around representative key points.
A Review of Deep Learning Techniques in Document Image Word Spotting
Lalita Kumari,Anuj Sharma +1 more
TL;DR: This study covers recent deep learning technique role in word spotting and future scope of word spotting with deep learning and an experimental comparison for the research community to evaluate algorithmic advances along with benchmarked datasets, and future challenges in this field.
11
A Framework for Efficient Transcription of Historical Documents Using Keyword Spotting
Konstantinos Zagoris,Ioannis Pratikakis,Basilis Gatos +2 more
- 22 Aug 2015
TL;DR: In the proposed framework, KWS is coupled with a relevance feedback mechanism which further enhances retrieval performance while being independent to the chosen KWS algorithm, thus, reducing drastically the cost of training data creation.
7
Multi-sensor event detection using shape histograms
Ehtesham Hassan,Gautam Shroff,Puneet Agarwal +2 more
- 18 Mar 2015
TL;DR: This paper defines the shape histogram: a constant dimension descriptor that nevertheless captures patterns of variable duration that is the most versatile in terms of how it ranks compared to other published results.
6
References
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
- 01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
53.5K
•Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester,Hans-Peter Kriegel,Jörg Sander,Xiaowei Xu +3 more
- 02 Aug 1996
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
20.3K
•Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
Martin Ester,Hans-Peter Kriegel,Jörg Sander,Xiaowei Xu +3 more
- 01 Jan 1996
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Shape matching and object recognition using shape contexts
TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
7.3K