An Improved Scene Text Extraction Method Using Conditional Random Field and Optical Character Recognition

2011 ◽  
Author(s):  
Hongwei Zhang ◽  
Changsong Liu ◽  
Cheng Yang ◽  
Xiaoqing Ding ◽  
KongQiao Wang
Keyword(s):  
Random Field ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Conditional Random Field ◽  
Text Extraction ◽  
Optical Character ◽  
Scene Text

Multi-Oriented Text Extraction in Stylistic Documents

2015 ◽  
Vol 15 (01) ◽  
pp. 1550002
Author(s):  
Brij Mohan Singh ◽  
Rahul Sharma ◽  
Debashis Ghosh ◽  
Ankush Mittal
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Morphological Operations ◽  
Document Images ◽  
Font Size ◽  
Text Extraction ◽  
Optical Character ◽  
Engineering Drawings ◽  
Flood Fill

In many documents such as maps, engineering drawings and artistic documents, etc. there exist many printed as well as handwritten materials where text regions and text-lines are not parallel to each other, curved in nature, and having various types of text such as different font size, text and non-text areas lying close to each other and non-straight, skewed and warped text-lines. Optical character recognition (OCR) systems available commercially such as ABYY fine reader and Free OCR, are not capable of handling different ranges of stylistic document images containing curved, multi-oriented, and stylish font text-lines. Extraction of individual text-lines and words from these documents is generally not straight forward. Most of the segmentation works reported is on simple documents but still it remains a highly challenging task to implement an OCR that works under all possible conditions and gives highly accurate results, especially in the case of stylistic documents. This paper presents dilation and flood fill morphological operations based approach that extracts multi-oriented text-lines and words from the complex layout or stylistic document images in the subsequent stages. The segmentation results obtained from our method proves to be superior over the standard profiling-based method.

Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition

2021 ◽  
Vol 11 (4) ◽  
pp. 39-62
Author(s):  
Monica Gupta ◽  
Alka Choudhary ◽  
Jyotsna Parmar
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Short Term Memory ◽  
Handwriting Recognition ◽  
Text Recognition ◽  
Stroke Width ◽  
Optical Character ◽  
Scene Text ◽  
Benchmark Datasets ◽  
Scene Text Recognition

In today's era, data in digitalized form is needed for faster processing and performing of all tasks. The best way to digitalize the documents is by extracting the text from them. This work of text extraction can be performed by various text identification tasks such as scene text recognition, optical character recognition, handwriting recognition, and much more. This paper presents, reviews, and analyses recent research expansion in the area of optical character recognition and scene text recognition based on various existing models such as convolutional neural network, long short-term memory, cognitive reading for image processing, maximally stable extreme regions, stroke width transformation, and achieved remarkable results up to 90.34% of F-score with benchmark datasets such as ICDAR 2013, ICDAR 2019, IIIT5k. The researchers have done outstanding work in the text recognition field. Yet, improvement in text detection in low-quality image performance is required, as text identification should not be limited to the input quality of the image.

A Structured Method for the Recognition of Complex Historical Tables

1997 ◽  
Vol 9 (1-3) ◽  
pp. 58-77
Author(s):  
Vitaly Kliatskine ◽  
Eugene Shchepin ◽  
Gunnar Thorvaldsen ◽  
Konstantin Zingerman ◽  
Valery Lazarev
Keyword(s):  
Nineteenth Century ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Source Material ◽  
Historical Sources ◽  
Tax Assessment ◽  
Optical Character ◽  
Algorithmic Model ◽  
Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

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