CNN for historic handwritten document search

In our proposed work we will accept the challenges of recognizing the words and we will work to win the challenge. The handwritten document is scanned using a scanner. The image of the scanned document is processed victimization the program. Each character in the word is isolated. Then the individual isolated character is subjected to “Feature Extraction” by the Gabor Feature. Extracted features are passed through KNN classifier. Finally we get the Recognized word. Character recognition is a process by which computer recognizes handwritten characters and turns them into a format which a user can understand. Computer primarily based pattern recognition may be a method that involves many sub process. In today’s surroundings character recognition has gained ton of concentration with in the field of pattern recognition. Handwritten character recognition is beneficial in cheque process in banks, form processing systems and many more. Character recognition is one in all the favored and difficult space in analysis. In future, character recognition creates paperless environment. The novelty of this approach is to achieve better accuracy, reduced computational time for recognition of handwritten characters. The proposed method extracts the geometric features of the character contour. These features are based on the basic line types that forms the character skeleton. The system offers a feature vector as its output. The feature vectors so generated from a training set, were then used to train a pattern recognition engine based on Neural Networks so that the system can be benchmarked. The algorithm proposed concentrates on the same. It extracts totally different line varieties that forms a specific character. It conjointly also concentrates on the point options of constant. The feature extraction technique explained was tested using a Neural Network which was trained with the feature vectors obtained from the proposed method.

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TheyBuyForYou platform and knowledge graph: Expanding horizons in public procurement with open linked data

Semantic Web ◽

10.3233/sw-210442 ◽

2021 ◽

pp. 1-27

Author(s):

Ahmet Soylu ◽

Oscar Corcho ◽

Brian Elvesæter ◽

Carlos Badenes-Olmedo ◽

Tom Blount ◽

...

Keyword(s):

Linked Data ◽

Public Procurement ◽

Open Data ◽

Knowledge Graph ◽

Multiple Sources ◽

The Public ◽

Cross Border ◽

Data Platform ◽

Document Search ◽

Cross Lingual

Public procurement is a large market affecting almost every organisation and individual; therefore, governments need to ensure its efficiency, transparency, and accountability, while creating healthy, competitive, and vibrant economies. In this context, open data initiatives and integration of data from multiple sources across national borders could transform the procurement market by such as lowering the barriers of entry for smaller suppliers and encouraging healthier competition, in particular by enabling cross-border bids. Increasingly more open data is published in the public sector; however, these are created and maintained in siloes and are not straightforward to reuse or maintain because of technical heterogeneity, lack of quality, insufficient metadata, or missing links to related domains. To this end, we developed an open linked data platform, called TheyBuyForYou, consisting of a set of modular APIs and ontologies to publish, curate, integrate, analyse, and visualise an EU-wide, cross-border, and cross-lingual procurement knowledge graph. We developed advanced tools and services on top of the knowledge graph for anomaly detection, cross-lingual document search, and data storytelling. This article describes the TheyBuyForYou platform and knowledge graph, reports their adoption by different stakeholders and challenges and experiences we went through while creating them, and demonstrates the usefulness of Semantic Web and Linked Data technologies for enhancing public procurement.

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Status of document search and data quality objective efforts

10.2172/6992873 ◽

1992 ◽

Author(s):

S Gydesen

Keyword(s):

Data Quality ◽

Document Search ◽

Quality Objective

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Voting Based Text Line Segmentation in Handwritten Document Images

2010 10th IEEE International Conference on Computer and Information Technology ◽

10.1109/cit.2010.114 ◽

2010 ◽

Cited By ~ 2

Author(s):

Toan Nguyen Dinh ◽

Jonghyun Park ◽

GueeSang Lee

Keyword(s):

Text Line ◽

Document Images ◽

Handwritten Document ◽

Text Line Segmentation ◽

Line Segmentation

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A Fuzzy Algorithm for Optimizing Semantic Documental Searches

International Journal of Web Portals ◽

10.4018/ijwp.2014010104 ◽

2014 ◽

Vol 6 (1) ◽

pp. 50-63

Author(s):

Sara Paiva

Keyword(s):

Scientific Paper ◽

Search Algorithms ◽

Semantic Search ◽

Fuzzy Algorithm ◽

Internet Users ◽

Reference Manager ◽

Document Search ◽

Real Scenario

Search for documents is a common and pertinent task lots of organizations face every day as well as common Internet users in their daily searches. One specific document search is scientific paper search in reference manager systems such as Mendeley or IEEExplore. Considering the difficult task finding documents can sometimes represent, semantic search is currently being applied to improve this type of search. As the act of deciding if a document is a good result for a given search expression is vague, fuzziness becomes an important aspect when defining search algorithms. In this paper, the author present a fuzzy algorithm for improving documental searches optimized for specific scenarios where we want to find a document but don´t remember the exact words used, if plural or singular words were used or if a synonym was used. The author also present the application of this algorithm to a real scenario comparing to Mendeley and IEEExplore results.

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