Multi-Lingual Character Recognition and Extraction using Recurrent Neural Networks
A Novel Approach to Smart Autonomous Monitoring of Indoor Plant Health Based on Leaf Color
Edge Preserving Image Denoising with Deep Networks and Ensemble Convolutional Neural Network Architectures
Neural Network Approach to Detect Renal Calculi
Enhancing Fabric Quality Control: Implementing Real-Time Defect Detection with Image Processing Techniques and Arduino
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
In recent years, segmentation and recognition of multilingual languages have attracted the attention of many researchers. Multilingual Optical Character Recognition (OCR) technology uses tools like PyTesseract, OpenCV and Recurrent Neural Networks (RNN) to transform text in English, Telugu, Hindi, Tamil and Kannada. Converting text to digital format transforms communication and supports cultural understanding. The system supports multiple languages and can handle different languages. PyTesseract and OpenCV are used for accurate behavior recognition, while RNN improves language understanding. To ensure accuracy, the system uses advanced techniques to overcome problems such as noise and distortion in data input. This technology, combined with advanced OCR algorithms, improves text recognition and makes it adaptable to multilingual environments. This study highlights the importance of multilingual OCR in preserving language, supporting international cooperation, and encouraging participation in the digital age. The research explores ways to use cross-language grammar, fonts, and document layouts using previously implemented techniques to create informative content. RNN further improves the OCR process by capturing complex words. The userfriendly interface and integration with various platforms increase accessibility, allowing users to easily engage with multilingual content. Therefore, multilingual OCR, which combines PyTesseract, OpenCV, RNN, and other advanced techniques, is used to overcome speech problems, handle various grammars and input data, and have a positive impact on the development of OCR technology. This research helps create a globally connected society where knowledge is transmitted across language boundaries, fostering cultural exchange and fostering growth, while ensuring a good and accurate understanding of literature.
Automation of technology is being implemented in various fields, including deep space research and the automobile industry. However, the real necessity for automation is identified in the agricultural sector. Therefore, this paper focuses on addressing this requirement and is primarily concerned with automating the determination of a plant's health based on the color content present in any one of its leaves, while simultaneously monitoring it. A plant's leaf is examined, and the algorithm makes the decision of whether to provide water and light whenever there is a notable change in the leaf's color. The automation of watering and lighting is explained in detail in the proposed methodology. This paper explores the vital role of automation in agriculture, focusing on using color analysis of plant leaves to automate health assessment. The algorithm used monitors and responds to color changes, enabling informed decisions on water and light provision for optimal plant growth.
In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network's understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, and main part of the image or to suppress image noise and high-frequency interference components, which can make the image's brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. Reducing noise is treated as one of the important problems in image processing. At the same time, preserving the edges of objects is of critical importance to protect the visual appearance of the objects. Deep networks have marked a trend in computer vision applications and this paper presents a customized Gaussian noise minimizing network with edge preserving filers. Here, ensemble architecture of convolutional neural network is used in minimizing the Gaussian noise and image denoising. The ensemble architecture is combined with VGG-19 and Xception design of CNN. The ensemble Convolutional Neural Networks (CNNs) are classified for Gaussian noisy images, real noisy images, blind denoising, and hybrid noisy images, representing the combination of noisy, blurred, and lowresolution images. Following the classification, motivations and principles of various deep learning methods are analyzed. Subsequently, a comparison of state-of-the-art methods on public denoising datasets is conducted, considering both quantitative and qualitative analyses. The experimental analysis is carried out in terms of PSNR, accuracy, precision, recall and F-measure.
Back Propagation Network is the most commonly used algorithm in training neural networks. It is employed in processing the images and data to implement an automated kidney stone classification. The conventional technique for classifying medical resonance kidney images and detecting stones relies on human examination. This method is not accurate since it is impractical to handle large amount of data. Magnetic Resonance (MR) Images may inherently possess noise caused by operator errors. This causes earnest inaccuracies in classification features and diseases in image processing. However, the usage of artificial intelligent based methods along with neural networks and feature extraction has shown great potential in extracting the region of interest using back propagation network algorithms in this field. In this work, the Back Propagation Network was applied for the objective of kidney stone detection. Decision-making is carried out in two stages, Feature extraction and Image classification. The feature extraction is done using the principal component analysis and the image classification is done using Back Propagation Network (BPN). This work presents a segmentation method using the Fuzzy C-Mean (FCM) clustering algorithm. The performance of the BPN classifier was estimated in terms of training execution and classification accuracy. The Back Propagation Network gives precise classification when compared to other methods based on neural networks.
The textile industry is a rapidly growing sector globally and plays a momentous role in many sectors like manufacturing, employment, and business operations in many developed countries. Cloth flaws account for over 85% of the failures experienced in the garment industry. Efforts are currently underway to enhance fabric consistency, making the identification of defects a critical step in the textile manufacturing process. However, the traditional manual inspection technique for detecting cloth flaws is time-consuming and labor-consuming. Consequently, automation has been introduced through image processing technology. This approach utilizes image processing techniques in MATLAB to locate faults, with fault detection carried out using an Arduino. To improve the accuracy of fabric defect identification, an electronic fabric inspection method has been proposed. This framework incorporates image processing techniques, employing MATLAB, and real-time applications implemented on the Arduino kit. Neural Networks serve as the optimal classifiers for fault classification. Upon detecting a flaw in the fabric, the system breaks shortly to remove the defective component. The identified fault is then displayed on the LCD, and the buzzer is activated.
