A Duplicate Chinese Document Image Retrieval System

An optical character recognition (OCR) system enables a user to feed an article directly into an electronic computer file and translate the optically scanned bitmaps of text characters into machine-readable codes; that is, ASCII, Chinese GB, as well as Big5 codes, and then edits it by using a word processor. OCR is hence being employed by libraries to digitize and preserve their holdings. Billions of letters are sorted every day by OCR machines, which can considerably speed up mail delivery. The techniques of OCR can be divided into two approaches: template matching and structure analysis (Mori, Suen & Yamamoto, 1992). The template matching approach is to reduce the complexity of matching by projecting from two-dimensional information onto one; the structure analysis approach is to analyze the variation of shapes of characters. The template matching approach is only suitable for recognizing printed characters; however, the structure analysis approach can be applied to recognize handwritten characters. Several OCR techniques have been proposed, based on statistical, matching, transform and shape features (Abdelazim & Hashish, 1989; Papamarkos, Spilioties & Zoumadakis, 1994). Recently, integrated OCR systems have been proposed, and they take advantage of specific character- driven hardware implementations (Pereira & Bourbakis, 1995). OCR generally involves four discrete processes (Khoubyari & Hull, 1996; Liu, Tang & Suen, 1997; Wang, Fan & Wu, 1997): 1. separate the text and the image blocks; then finds columns, paragraphs, text lines, words, and characters; 2. extract the features of characters, and compare their features with a set of rules that can distinguish each character/font from others; 3. correct the incorrect words by using spell checking tools; and 4. translate each symbol into a machine-readable code.

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Document image retrieval system using character candidates generated by character recognition process

Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93) ◽

10.1109/icdar.1993.395677 ◽

2002 ◽

Cited By ~ 8

Author(s):

S. Senda ◽

M. Minoh ◽

K. Ikeda

Keyword(s):

Image Retrieval ◽

Character Recognition ◽

Retrieval System ◽

Document Image ◽

Recognition Process ◽

Image Retrieval System

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Hangul document image retrieval system using rank-based recognition

Eighth International Conference on Document Analysis and Recognition (ICDAR'05) ◽

10.1109/icdar.2005.126 ◽

2005 ◽

Author(s):

Duk-Ryong Lee ◽

Woo-Youn Kim ◽

Il-Seok Oh

Keyword(s):

Image Retrieval ◽

Retrieval System ◽

Document Image ◽

Image Retrieval System

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A REVIEW ON MULTIQUERY SYSTEM FOR CONTENT BASED IMAGE RETRIEVAL

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v15i13.4800 ◽

2016 ◽

Vol 15 (13) ◽

pp. 7342-7346

Author(s):

Meenu Meenu ◽

Sonika Jindal

Keyword(s):

Image Retrieval ◽

Visual Information ◽

Retrieval System ◽

Image Similarity ◽

Content Based Image Retrieval ◽

Color Similarity ◽

Image Retrieval System ◽

Speed Up ◽

Similarity Computation ◽

Main Components

In recent years, very large collections of images and videos have grown rapidly. In parallel with this growth, content-based retrieval and querying the indexed collections are required to access visual information. Two of the main components of the visual information are texture and color. In this thesis, a content-based image retrieval system is presented that computes texture and color similarity among images. Content based image retrieval from large resources has become an area of wide interest now a days in many applications.Â To speed up retrieval and similarity computation, the database images are analysed and the extracted regions are clustered according to their feature vectors. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity.

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