An Unsupervised and Robust Line and Word Segmentation Method for Handwritten and Degraded Printed Document

2022 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Jayati Mukherjee ◽  
Swapan K. Parui ◽  
Utpal Roy
Keyword(s):  
Quantitative Measure ◽  
Classification Problem ◽  
Input Image ◽  
Document Image ◽  
Segmentation Method ◽  
Analysis Problem ◽  
Document Structure ◽  
Computational Resources ◽  
Degraded Document ◽  
Document Page

Segmentation of text lines and words in an unconstrained handwritten or a machine-printed degraded document is a challenging document analysis problem due to the heterogeneity in the document structure. Often there is un-even skew between the lines and also broken words in a document. In this article, the contribution lies in segmentation of a document page image into lines and words. We have proposed an unsupervised, robust, and simple statistical method to segment a document image that is either handwritten or machine-printed (degraded or otherwise). In our proposed method, the segmentation is treated as a two-class classification problem. The classification is done by considering the distribution of gap size (between lines and between words) in a binary page image. Our method is very simple and easy to implement. Other than the binarization of the input image, no pre-processing is necessary. There is no need of high computational resources. The proposed method is unsupervised in the sense that no annotated document page images are necessary. Thus, the issue of a training database does not arise. In fact, given a document page image, the parameters that are needed for segmentation of text lines and words are learned in an unsupervised manner. We have applied our proposed method on several popular publicly available handwritten and machine-printed datasets (ISIDDI, IAM-Hist, IAM, PBOK) of different Indian and other languages containing different fonts. Several experimental results are presented to show the effectiveness and robustness of our method. We have experimented on ICDAR-2013 handwriting segmentation contest dataset and our method outperforms the winning method. In addition to this, we have suggested a quantitative measure to compute the level of degradation of a document page image.

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

Experiment of OCITN: Considering Appropriate Goal Images and Metric for One-Class Image Transformation Network

2021 ◽  
Author(s):  
Toshitaka Hayashi ◽  
Hamido Fujita
Keyword(s):  
Image Data ◽  
Classification Problem ◽  
Model Error ◽  
Input Image ◽  
Training Data ◽  
Distance Metric ◽  
Image Transformation ◽  
Image Entropy ◽  
Image Metrics ◽  
One Class Classification

One-class classification (OCC) is a classification problem where training data includes only one class. In such a problem, two types of classes exist, seen class and unseen class, and classifying these classes is a challenge. Besides, One-class Image Transformation Network (OCITN) is an OCC algorithm for image data. In which, image transformation network (ITN) is trained. ITN aims to transform all input image into one image, namely goal image. Moreover, the model error of ITN is computed as a distance metric between ITN output and a goal image. Besides, OCITN accuracy is related to goal image, and finding an appropriate goal image is challenging. In this paper, 234 goal images are experimented with in OCITN using the CIFAR10 dataset. Experiment results are analyzed with three image metrics: image entropy, similarity with seen images, and image derivatives.

Script Identification of Camera Based Bilingual Document Images Using SFTA Features

2022 ◽  
pp. 811-822
Author(s):  
B.V. Dhandra ◽  
Satishkumar Mallappa ◽  
Gururaj Mukarambi
Keyword(s):  
Block Size ◽  
Texture Features ◽  
Input Image ◽  
Document Image ◽  
Optimal Size ◽  
Document Images ◽  
Binary Images ◽  
Script Identification ◽  
Unified Algorithm ◽  
Block Sizes

In this article, the exhaustive experiment is carried out to test the performance of the Segmentation based Fractal Texture Analysis (SFTA) features with nt = 4 pairs, and nt = 8 pairs, geometric features and their combinations. A unified algorithm is designed to identify the scripts of the camera captured bi-lingual document image containing International language English with each one of Hindi, Kannada, Telugu, Malayalam, Bengali, Oriya, Punjabi, and Urdu scripts. The SFTA algorithm decomposes the input image into a set of binary images from which the fractal dimension of the resulting regions are computed in order to describe the segmented texture patterns. This motivates use of the SFTA features as the texture features to identify the scripts of the camera-based document image, which has an effect of non-homogeneous illumination (Resolution). An experiment is carried on eleven scripts each with 1000 sample images of block sizes 128 × 128, 256 × 256, 512 × 512 and 1024 × 1024. It is observed that the block size 512 × 512 gives the maximum accuracy of 86.45% for Gujarathi and English script combination and is the optimal size. The novelty of this article is that unified algorithm is developed for the script identification of bilingual document images.

SKEW CORRECTION OF DOCUMENT IMAGES BY RANK ANALYSIS IN FAREY SEQUENCE

2013 ◽  
Vol 27 (07) ◽  
pp. 1353004 ◽  
Author(s):  
SANJOY PRATIHAR ◽  
PARTHA BHOWMICK ◽  
SHAMIK SURAL ◽  
JAYANTA MUKHOPADHYAY
Keyword(s):  
Principal Direction ◽  
Estimation Algorithm ◽  
Document Image ◽  
Document Images ◽  
Skew Angle ◽  
Farey Sequence ◽  
Rank Analysis ◽  
Skew Correction ◽  
Edge Points ◽  
Document Page

Skew correction of a scanned document page is an important preprocessing step in document image analysis. We propose here a fast and robust skew estimation algorithm based on rank analysis in Farey sequence. Our target document class comprises two major Indian scripts with headlines, namely Devnagari and Bangla. At the beginning, straight edge segments from the edge map of the document page are detected by our algorithm using properties of digital straightness. Straight edges derived in this manner are binned by Farey ranks in correspondence with their slopes. The principal bin, identified from these bins using the strength of accumulated edge points, represents the principal direction along the direction of headlines, from which the gross skew angle is estimated. A fast refinement algorithm is then applied with a finer tuning of Farey ranks, to detect the skew up to the desired level of precision. The algorithm has been tested on a diverse set of document images, containing Bangla and Devnagari scripts. Experimental results are quite encouraging in terms of accuracy, sensitivity to non-textual objects, effectiveness in dealing with unrestricted layouts, and computational efficiency.

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