A New Text Extraction Method Incorporating Local Information

2012 ◽  
Author(s):  
Yang Zhang ◽  
Chunheng Wang ◽  
Baihua Xiao ◽  
Cunzhao Shi
Keyword(s):  
Extraction Method ◽  
Local Information ◽  
Text Extraction

Identification of Good One From the Damaged Crops/Fruits Using Decision-Level Information Matching

2019 ◽  
pp. 80-99
Author(s):  
Muthukumar Arunachalam ◽  
Meena Arunachalam
Keyword(s):  
Extraction Method ◽  
Gabor Filter ◽  
Local Information ◽  
Feature Extraction Method ◽  
Speeded Up Robust Features ◽  
Decision Level ◽  
Information Matching ◽  
Level Information ◽  
Orientation Information ◽  
Processing Techniques

Identification of useful items that can be picked up from the damaged crops or batch of fruits/vegetables is a challenging task nowadays. Humans may fail to identify them correctly with their naked eyes due to strain. Image processing techniques can help to maximize the amount of the good agro-items easily by comparing the existing goods to templates. This chapter introduces an effective recognition method to spot good agro-items by extracting the local features using Gabor filter for orientation information. Another local information of that fruit/vegetable is extracted by speeded up robust features (SURF) algorithm. The extracted features are matched with their templates which results in the decision of individual feature extraction method. Finally, both local information is fused at decision level individually with AND operation (i.e., both algorithms will give correct decision to identify the good agro-item).

A Novel Text Extraction Method from Pure Text Images Using Morphological Operations

2014 ◽  
Vol 989-994 ◽  
pp. 3768-3772
Author(s):  
Xuan Qi Chen ◽  
Biao He ◽  
Guo Cheng Wang ◽  
Yao Xin Li
Keyword(s):  
Mathematical Morphology ◽  
Extraction Method ◽  
New Method ◽  
Morphological Operations ◽  
Text Extraction ◽  
Text Images ◽  
Robust To Noise

This paper presents a new method to achieve effective text extraction using mathematical morphology. Firstly, the document is segmented and divided into several parts based on the layout. And then, every part is dilated to big connected regions, whose biggest skeleton will be extracted and serve as a structure element (SE). Finally, a proposed region-concatenated operation with the SE will be employed, whose result can be the input of subsequent OCR system. Experimentally, the proposed method is robust to noise, the text orientation, font style and size, language and layout.

Sign in / Sign up

Export Citation Format

Share Document