Skew Detection and Correction in Text Document Image using Projection Profile Technique

2018 ◽  
Vol 6 (7) ◽  
pp. 986-990
Author(s):  
Rubani . ◽  
Jyoti Rani
Keyword(s):  
Document Image ◽  
Projection Profile ◽  
Skew Detection ◽  
Text Document ◽  
Profile Technique

scholarly journals Skew detection based on vertical projection in latin character recognition of text document image

2018 ◽  
Vol 7 (4.44) ◽  
pp. 198
Author(s):  
Ronny Susanto ◽  
Farica P. Putri ◽  
Y. Widya Wiratama
Keyword(s):  
Error Rate ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Rotation Angle ◽  
Document Image ◽  
Computational Time ◽  
Vertical Projection ◽  
Word Error Rate ◽  
Skew Detection ◽  
Text Document

The accuracy of Optical Character Recognition is deeply affected by the skew of the image.  Skew detection & correction is one of the steps in OCR preprocessing to detect and correct the skew of document image. This research measures the effect of Combined Vertical Projection skew detection method to the accuracy of OCR. Accuracy of OCR is measured in Character Error Rate, Word Error Rate, and Word Error Rate (Order Independent). This research also measures the computational time needed in Combined Vertical Projection with different iteration. The experiment of Combined Vertical Projection is conducted by using iteration 0.5, 1, and 2 with rotation angle within -10 until 10 degrees. The experiment results show that the use of Combined Vertical Projection could lower the Character Error Rate, Word Error Rate, and Word Error Rate (Order Independent) up to 35.53, 34.51, and 32.74 percent, respectively. Using higher iteration value could lower the computational time but also decrease the accuracy of OCR.   

Skew Detection of Scanned Document Image Based on Shearlet Transform

2018 ◽  
Vol 55 (1) ◽  
pp. 011007
Author(s):  
张新红 Zhang Xinhong ◽  
张一凡 Zhang Yifan ◽  
张帆 Zhang Fan
Keyword(s):  
Document Image ◽  
Shearlet Transform ◽  
Skew Detection

A ROBUST SYSTEM FOR THRESHOLDING AND SKEW DETECTION IN MIXED TEXT/GRAPHICS DOCUMENTS

2005 ◽  
Vol 05 (02) ◽  
pp. 247-265 ◽  
Author(s):  
ADNAN AMIN ◽  
SUE WU
Keyword(s):  
Automatic System ◽  
Real Life ◽  
Detection Algorithm ◽  
Document Image ◽  
Superior Performance ◽  
Document Images ◽  
Skew Detection ◽  
Robust System ◽  
Graphic Images ◽  
Scanned Images

This article presents an automatic system that takes in grayscale scanned images, which could be mixed text/graphic documents, and performs thresholding and skew detection on the document images. The system consists of two major components; multistage thresholding and skew detection. The proposed skew detection algorithm has no restriction on detectable angle range and does not rely on large blocks of text. It works well on textual document images, graphical images and mixed text and graphic images. The performance of the systems was evaluated using over 60 images that consist of real life documents like envelopes and artificial mixed text/graphic icons. The superior performance of thresholding is clear compared to other techniques from the evaluation. The skew detection algorithm is robust when compared with other methods when very few text lines are present in the document image.

scholarly journals A Novel Noise-removal Technique for Document Images

2012 ◽  
pp. 65-69
Author(s):  
Samir Malakar ◽  
Dheeraj Mohanta ◽  
Ram Sarkar ◽  
Mita Nasipuri
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Noise Removal ◽  
Document Image ◽  
Free Text ◽  
Median Filters ◽  
Text Document ◽  
Modal Class ◽  
Filtering Technique ◽  
Removal Technique

For developing a high quality Optical Character Recognition (OCR) system removal of noise from the document image is an utmost important step. To make this possible, filtering plays a significant role. Although mean and median filters, the two well-known statistical filtering techniques, are used commonly but sometimes these filters may fail to produce noise-free images or sometimes may introduce distortions on the characters in the form of gulfs or capes. In the work reported here, we have developed a new filtering technique, called Middle of Modal Class (MMC), for smoothing the input images. This filtering technique is applicable for both the noisy and noise free text document image at the same time. We have also compared our results with mean and median filters, and have achieved better result.

scholarly journals LANGUAGE INDEPENDENT ROBUST SKEW DETECTION AND CORRECTION TECHNIQUE FOR DOCUMENT IMAGES

2012 ◽  
pp. 111-115
Author(s):  
Neha. N
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Document Analysis ◽  
Document Image ◽  
Document Images ◽  
Novel Technique ◽  
Skew Detection ◽  
Optical Character ◽  
Correction Technique ◽  
All Optical

Document image processing is an increasingly important technology essential in all optical character recognition (OCR) systems and for automation of various office documents. A document originally has zero-skew (tilt), but when a page is scanned or photo copied, skew may be introduced due to various factors and is practically unavoidable. Presence even a small amount of skew (0.50) will have detrimental effects on document analysis as it has a direct effect on the reliability and efficiency of segmentation, recognition and feature extraction stages. Therefore removal of skew is of paramount importance in the field of document analysis and OCR and is the first step to be accomplished. This paper presents a novel technique for skew detection and correction which is both language and content independent. The proposed technique is based on the maximum density of the foreground pixels and their orientation in the document image. Unlike other conventional algorithms which work only for machine printed textual documents scripted in English, this technique works well for all kinds of document images (machine printed, hand written, complex, noisy and simple). The technique presented here is tested with 150 different document image samples and is found to provide results with an accuracy of 0.10

scholarly journals Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features

2019 ◽  
Vol 8 (2) ◽  
pp. 2988-2993
Keyword(s):  
English Language ◽  
Feature Vector ◽  
Video Camera ◽  
Input Image ◽  
Document Image ◽  
Gray Level ◽  
Indian Languages ◽  
Binary Images ◽  
Script Identification ◽  
Text Document

Most of the documents in various application areas like Government, Business and Research are available in the form of bi-lingual/multi-lingual text document. The multilingual documents are captured from video/camera for identification of script of the text document for automatic reading and editing. In this paper, an attempt is made to address the problem of script identification from camera captured document images using SFTA features. The input image is decomposed into a group of binary images by applying TTBD with fixing the number of the threshold as t n =3 empirically, on each decomposed binary image, Box Count, Mean Gray Level, and Pixel Count are extracted to form the feature vector. This feature vector is submitted to K-NN classifier to identify the scripts of the input document image. In all 10 scripts of the Indian languages are considered along with common English language as bi-lingual documents. The novelty of the paper is that 7 features are selected as potential features to obtain the highest accuracy. Features like Box Count (3), Mean Gray Level (2), and Pixel Count (2) have obtained the 87.02% recognition accuracy for English and Hindi Script combinations for the collected dataset and encouraging results for other combinations. These 7 potential features were selected using the technique named as feed-forward feature selection, from the set all 18 features.

Sign in / Sign up

Export Citation Format

Share Document