Degraded Document Image Binarization Using Segmentation Algorithm

Degraded document image binarization is very difficult process due to different types of degradation over the document. Multiple algorithms as well as methods are available to get clear image of degraded document image. Many researchers have worked in this field of image processing. Still there is scope to get more clear and upgraded document image. Image segmentation is very famous process in the image processing domain. Image segmentation can used to binarize degraded document image. Binarization is a process to generate binary image from gray scale image. Also it is tedious to differentiate foreground and background pixel due to degradation. In this paper, Image Segmentation using thresholding is proposed for degraded document image binarization. Image segmentation gives better result than canny edge approach. DOI: 10.17762/ijritcc2321-8169.150611

Cross-entropy based image thresholding

This paper presents a novel global thresholding algorithm for the binarization of documents and gray-scale images using Cross Entropy Clustering. In the first step, a gray-level histogram is constructed, and the Gaussian densities are fitted. The thresholds are then determined as the cross-points of the Gaussian densities. This approach automatically detects the number of components (the upper limit of Gaussian densities is required)

Use of adaptive methods to improve degraded document images

An adaptive method for enhancing and binarizing the images of degraded documents has been presented here. This method does not need any feature handling by the user and it handles all kinds of degradations, removes noise, ensures connectivity of stroke and improves low-contrast. The project briefly includes following steps: a pre-processing procedure using a low-pass Wiener filter to produce a smoothened image, an approximate estimation of foreground regions,a background surface calculation by interpolating neighboring background intensities, a thresholding by combining the calculated background surface with the original image followed by a post-processing step which is carried out to enhance the quality of foreground regions and preserve line connectivity of texts

Improved wolf algorithm on document images detection using optimum mean technique

Detection text from handwriting in historical documents provides high-level features for the challenging problem of handwriting recognition. Such handwriting often contains noise, faint or incomplete strokes, strokes with gaps, and competing lines when embedded in a table or form, making it unsuitable for local line following algorithms or associated binarization schemes. In this paper, a proposed method based on the optimum threshold value and namely as the Optimum Mean method was presented. Besides, Wolf method unsuccessful in order to detect the thin text in the non-uniform input image. However, the proposed method was suggested to overcome the Wolf method problem by suggesting a maximum threshold value using optimum mean. Based on the calculation, the proposed method obtained a higher F-measure (74.53), PSNR (14.77) and lowest NRM (0.11) compared to the Wolf method. In conclusion, the proposed method successful and effective to solve the wolf problem by producing a high-quality output image

Binarisation Algorithms Analysis on Document and Natural Scene Images

The binarisation plays an important role in a system for text extraction from images which is a prominent area in digital image processing. The primary goal of the binarisation techniques are to covert colored and gray scale image into black and white image so that overall computational overhead can be minimized. It has great impact on performance of the system for text extraction from image. Such system has number of applications like navigation system for visually impaired persons, automatic text extraction from document images, and number plate detection to enforcement traffic rules etc. The present study analysed the performance of well known binarisation algorithms on degraded documents and camera captured images. The statistical parameters namely Precession, Recall and F-measure and PSNR are used to evaluate the performance. To find the suitability of the binarisation method for text preservation in natural scene images, we have also considered visual observation DOI: 10.17762/ijritcc2321-8169.15083

Sensitivity to water deficit of the second stage of fruit growth in late mandarin trees

In citrus fruits, phases I and II of fruit growth are sensitive to water deficit, and for this reason, deficit irrigation (DI) has been usually restricted to the final ripening phase. However, the optimal timing and intensity of stress during sensitive phases have not been clearly defined. The main objective was to determine the sensitivity of the second stage of fruit growth to water deficit in adult mandarin trees, and to explore the suitability of different soil and plant water status indicators, including the leaf-scale spectrum, according to the water stress level. Four irrigation treatments were tested: a control (CTL) irrigated at ~ 80% of ETc during the entire crop cycle, and three irrigation suppression treatments, in which no water was applied during the end of phase I and the beginning of phase II (DI1), the second half of phase II (DI2), and phase III of fruit growth (DI3), respectively. Phase II of fruit growth can be considered as a non-critical phenological period until the fruit reaches approximately 60% of its final size, with the application of a water deficit using an irrigation threshold of midday stem water potential of − 1.8 MPa, and a cumulative water stress integral close to 28 MPa day. The novel visible infrared ratio index (VIRI) showed a high sensitivity for trees subjected to moderate and severe water stress and can be complementarily used to estimate on a larger temporal and spatial scale the plant water status. Wavelengths in the short-wave infrared (SWIR) region allowed differentiation between non-stressed, moderately, and severely water-stressed trees, and can be considered as an initial basis for determining the water status of mandarin trees at various stress intensities by remote sensing.This study was supported by the European Commission H2020 (Grant 728003, DIVERFARMING Project) and National Research Agency of Spain (PID2019-106226RB-C22)

Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping

Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants’ responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology