The offline character recognition is very useful software in the field of research. Authoritative field of research has made by character recognition due to its need in various fields of research as in banks, post offices to fulfill all recognition requirements. This paper is an exploration on the different scripts including Mathematical digits, Hindi consonants and vowels, Gurumukhi characters as well as numerals. Different handwritten samples of all these language scripts are taken from different persons in a good environment. First of all preprocessing including different operations for the reduction of noise is operated. The pre-processed documents are then segmented for the feature extraction. In this technique, different features like lines, corners etc. are to be extracted. Number of hidden layers taken are 15. Then neural network is trained for the testing of all these handwritten samples. Out of 100% samples, 90% are used for training, 5% are for testing and the remaining of the samples are used for validation. Comparison of recognition rates achieved on different scripts is accessible. Out of all samples taken, highest accuracy is achieved on the Gurumukhi script which is 99.9%.

/pdf/offline-handwritten-character-recognition-using-neural-2tt1uc2hp1.pdf

Offline Handwritten Character Recognition using Neural Network

Bengali, is not only a major language in two states West Bengal, and Tripura of India, but it is the main language of an Entire country Bangladesh. Having such large application areas of Bengali language, and with the evolution of automation, machine learning, and trending technological development, it is also high time to make a complete product which can easily and efficiently recognize Bengali handwritten characters. Research in this area is attracting attention day by day, and there are the obvious reasons. Application area of such Bengali handwritten character recognition system is undoubtedly very high, but unfortunately, there is not such success in research, which can produce a complete operational system that can be commercialized or practically implemented in day to day life. The current work is a study of the different tasks to be done along with achievements throughout the last decade for different tasks in the field of handwritten Bengali character recognition.

Pattern Recognition based Tasks and Achievements on Handwritten Bengali Character Recognition

Handwritten Text Recognition is an important area of research because of growing demand to process and convert a huge data and information available in handwritten form to Digital form. The digital data instead of handwritten form can prove to be highly useful in different fields. Handwritten text recognition plays an important role in applications involved in, postal services, banks for cheque processing, searching of information and organization dealing with such applications. In text recognition application dataset of the specified script is required for training purpose. Datasets of the different languages could be found online but dataset of dogra script characters is still not available. This paper presents a Dogra handwriting character dataset which contains around 38690 character images etc grouped in 73 character classes extracted from 530 one-page handwritings of 265 individuals of having variable age, sex, qualification, location. The dogra character dataset would be freely accessible by scholars and researchers which could also be used for further recognition improvement and updating with more characters and word, Identification of writer, dogra word segmentation. Dogra dataset could also be used for extracting variation of handwriting according to age and gender.

DograNet – a comprehensive offline dogra handwriting character dataset

DOI: 10.5281/zenodo.60830 ABSTRACT A Handwritten character recognition (HCR) is an important task of detecting and recognizing in characters from the input digital image and convert it to other equivalent machine editable form. It gives high growth in image processing and pattern recognition. It has big challenges in data interpretation from language identification, bank cheques and conversion of any handwritten document into structural text form. Handwritten character recognition system uses a soft computing method like neural network, having area of research for long time with multiple theories and developed algorithm. Feature Extraction done in character recognition by introducing a new approach, diagonal based feature extraction. We used two Dataset, first one is own database of 26 alphabets, 10 numbers and 5 special characters written by various people and second is standard CEDAR database. The character recognition is carried out by supervised KNN classifier and LVQ. The results show that KNN has better results than LVQ. .

/pdf/handwritten-english-character-recognition-using-lvq-and-knn-ch37mddymy.pdf

Handwritten english character recognition using lvq and knn

Enhanced Detection of Diabetic Retinopathy Using Ensemble Machine Learning: A Comparative Study

: Diabetic retinopathy is one of the most serious eye diseases and can lead to permanent blindness if not diagnosed early. The main cause of this is diabetes. Not every diabetic will develop diabetic retinopathy, but the risk of developing diabetes is undeniable. This requires the early diagnosis of Diabetic retinopathy. Segmentation is one of the approaches which is useful for detecting the blood vessels in the retinal image. This paper proposed the three models based on a deep learning approach for recognizing blood vessels from retinal images using region-based segmentation techniques. The proposed model consists of four steps preprocessing, Augmentation, Model training, and Performance measure. The augmented retinal images are fed to the three models for training and finally, get the segmented image. The proposed three models are applied on publically available data set of DRIVE, STARE, and HRF. It is observed that more thin blood vessels are segmented on the retinal image in the HRF dataset using model-3. The performance of proposed three models is compare with other state-of-art-methods of blood vessels segmentation of DRIVE, STARE, and HRF datasets.

/pdf/retinal-image-segmentation-for-diabetic-retinopathy-3g6m9gl5.pdf

Retinal Image Segmentation for Diabetic Retinopathy Detection using U-Net Architecture

Character recognition, both handwritten and printed, is still a challenge to the researchers because of its importance. Varity of character patterns with their style of writings is the main constraint of reliable recognition. Different neural network models are being used to overcome this difficulty. In this paper, a new approach, named, Block wise Segmentation Technique (BST) has been used to prepare the input character matrix to train a neural network model. BST is the representation of input in terms of a number of equal sized vectors derived from the input matrix. On testing, the model produces a high level of accuracy to identify deviated characters also. Keywords— Character recognition, Artificial Neural Network, Block wise Segmentation Technique, Training rule, Perceptron.

Handwritten Character Recognition Using Block wise Segmentation Technique (BST) in Neural Network

The ability of a machine to interpret handwritten characters from sources like paper document, photograph, etc. to some editable computerized form is the first and foremost aim of handwritten character recognition systems. Several challenges are there to construct this kind of systems, including the tasks eg. Digitization, segmentation, normalization, feature extraction, reorganization, and reconstruction. Here, an attempt has been made to recognize individual handwritten characters by the use of artificial neurons with feedback connection. The topology of the network is formed with two layers of artificial neurons, input and output. The neurons of output layer have a feedback connection from their output line.

/pdf/handwritten-character-recognition-with-feedback-neural-2cec7zuil1.pdf

Handwritten Character Recognition with Feedback Neural Network

The visibility of items in water is lower than that of those on land. Light waves from a source don't have enough time to reach an item before it vanishes beneath the surface because light waves in water travel more quickly than they do in air. As a result, it can be challenging for people to deal with water properly due to certain of its physical characteristics. In light of this, object detection underwater has a wide range of uses, including environmental monitoring, surveillance, search and rescue, and navigation. This might enhance the precision, efficiency, and safety of undersea activities. In light of the aforementioned, we proposed a deep-learning technique that can detect and classify a variety of underwater objects.

Innovations in Marine Robotics: Object Detection and Localization Underwater

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