Novel Heuristic Recurrent Neural Network Framework to Handle Automatic Telugu Text Categorization from Handwritten Text Image

In the near future, the digitization and processing of the current paper documents describe efficient role in the creation of a paperless environment. Deep learning techniques for handwritten recognition have been extensively studied by various researchers. Deep neural networks can be trained quickly thanks to a lot of data and other algorithmic advancements. Various methods for extracting text from handwritten manuscripts have been developed in literature. To extract features from written Telugu Text image having some other neural network approaches like convolution neural network (CNN), recurrent neural networks (RNN), long short-term memory (LSTM). Different deep learning related approaches are widely used to identification of handwritten Telugu Text; various techniques are used in literature for the identification of Telugu Text from documents. For automatic identification of Telugu written script efficiently to eliminate noise and other semantic features present in Telugu Text, in this paper, proposes Novel Heuristic Advanced Neural Network based Telugu Text Categorization Model (NHANNTCM) based on sequence-to-sequence feature extraction procedure. Proposed approach extracts the features using RNN and then represents Telugu Text in sequence-to-sequence format for the identification advanced neural network performs both encoding and decoding to identify and explore visual features from sequence of Telugu Text in input data. The classification accuracy rates for Telugu words, Telugu numerals, Telugu characters, Telugu sentences, and the corresponding Telugu sentences were 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% consequently. Experimental evaluation describe extracted with revealed which are textured i.e. TENG shown considerable operations in applications such as private information protection, security defense, and personal handwriting signature identification

Uncovering the myth of learning to read Chinese characters: phonetic, semantic, and orthographic strategies used by Chinese as foreign language learners

Oral Session - 6A: Lexical modeling: no. 6A.3Chinese is considered to be one of the most challenging orthographies to be learned by non-native speakers, in particular, the character. Chinese character is the basic reading unit that converges sound, form and meaning. The predominant type of Chinese character is semantic-phonetic compound that is composed of phonetic and semantic radicals, giving the clues of the sound and meaning, respectively. Over the last two decades, psycholinguistic research has made significant progress in specifying the roles of phonetic and semantic radicals in character processing among native Chinese speakers …postprin

(Dis)connections between specific language impairment and dyslexia in Chinese

Poster Session: no. 26P.40Specific language impairment (SLI) and dyslexia describe language-learning impairments that occur in the absence of a sensory, cognitive, or psychosocial impairment. SLI is primarily defined by an impairment in oral language, and dyslexia by a deficit in the reading of written words. SLI and dyslexia co-occur in school-age children learning English, with rates ranging from 17% to 75%. For children learning Chinese, SLI and dyslexia also co-occur. Wong et al. (2010) first reported on the presence of dyslexia in a clinical sample of 6- to 11-year-old school-age children with SLI. The study compared the reading-related cognitive skills of children with SLI and dyslexia (SLI-D) with 2 groups of children …postprin

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Artificial Intelligence in Invoice Recognition: a Systematic Literature Review

In the era marked by a flourishing economy and rapid advancements in information technology, the proliferation of invoice data has accentuated the urgent need for automated invoice recognition. Traditional manual methods, long relied upon for this task, have proven to be inefficient, error-prone, and incapable of coping with the rising volume of invoices. This research endeavours to addresses the imperative of automating invoice recognition by exploring, assessing, and advancing cutting-edge algorithms, techniques, and methods within the domain of Artificial Intelligence (AI). This research conducts a comprehensive Systematic Literature Review (SLR) to investigate Computer Vision (CV) approaches, encompassing image preprocessing, Layout Analysis (LA), Optical Character Recognition (OCR), and Information Extraction (IE). The objective is to provide valuable insights into these fundamental components of invoice recognition, emphasizing their significance in achieving accuracy and efficiency. This exploration aims to contribute to the development of more effective automated systems for extracting information from invoices, addressing the challenges posed by diverse formats and content. The results indicate that in LA, the combination of Mask Region-based Convolutional Neural Networks (M-RCNN) and Feature Pyramid Network (FPN) achieves goods results. In OCR, algorithms like Convolutional Recurrent Neural Network (CRNN), You Only Look Once version 4 (YOLOv4) and models inspired by M-RCNN and Faster Region-based Convolutional Neural Network (F-RCNN) with ResNetXt-101 as the backbone demonstrate remarkable performance. When it comes to IE, algorithms inspired by F-RCNN and Region Proposal Network (RPN), Grid Convolutional Neural Network (G-CNN) and Layer Graph Convolutional Networks (LGCN), and Gated Graph Convolutional Network (GatedGCN) consistently deliver the best results

Learning about me and you : Only deterministic stimulus associations elicit self-prioritization

Open Access via the Elsevier agreement Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Peer reviewedPublisher PD