An Improved Text Extraction Approach with Auto Encoder for Creating Your Own Audiobook

As we all know, listening makes learning easier and interesting than reading. An audiobook is a software that converts text to speech. Though this sounds good, the audiobooks available in the market are not free and feasible for everyone. Added to this, we find that these audiobooks are only meant for fictional stories, novels or comics. A comprehensive review of the available literature shows that very little intensive work was done for image to speech conversion. In this paper, we employ various strategies for the entire process. As an initial step, deep learning techniques are constructed to denoise the images that are fed to the system. This is followed by text extraction with the help of OCR engines. Additional improvements are made to improve the quality of text extraction and post processing spell check mechanism are incorporated for this purpose. Our result analysis demonstrates that with denoising and spell checking, our model has achieved an accuracy of 98.11% when compared to 84.02% without any denoising or spell check mechanism.

AI-Natural Language Processing (NLP)

Natural Language Processing (NLP) could be a branch of Artificial Intelligence (AI) that allows machines to know the human language. Its goal is to form systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification. Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It's spread its applications in various fields like computational linguistics, email spam detection, information extraction, summarization, medical, and question answering etc. The goal of the Natural Language Processing is to style and build software system which will analyze, understand, and generate languages that humans use naturally, so as that you just could also be ready to address your computer as if you were addressing another person. Because it’s one amongst the oldest area of research in machine learning it’s employed in major fields like artificial intelligence speech recognition and text processing. Natural language processing has brought major breakthrough within the sector of COMPUTATION AND AI.

An XML infrastructure

Spell checking has both practical and theoretical significance. The practical connections seem obvious: spell checking makes it easier to find some kinds of errors in documents. But spell checking is sometimes harder and less capable in XML than it could be. If a spell checker could exploit markup instead of just ignoring it, could spell checking be easier and more useful? The theoretical foundations of spell checking may be less obvious, but every spell checker operationalizes both a simple model of language and a model of errors and error correction. The SCX (spell checking for XML) framework is intended to support the author's experimentation with different models of language and errors: it uses XML technologies to tokenize documents, spell check them, provide a user interface for acting on the flags raised by the spell checker, and inserting the corrections into the original text.

Data Extraction and Sentimental Analysis from “Twitter” using Web Scrapping.

In this paper , we attempt to do the sentimental analysis of the 2016 US presidential elections. Sentimental analysis requires the data to be extracted from websites or sources where people present their opinions, views ,complaints about the subjects that need to analyzed .Furthermore, it is necessary to ensure that the sample size of the data is large enough to get conclusive results .It is also necessary to ensure that the data is cleaned before it is used to make predictions. Cleaning is done using common techniques like tokenization, spell check ,etc. Sentimental Analysis is one of the by-products of Natural Language Processing . This paper includes data collection as well as classification of textual data based on machine learning .

An Effective Preprocessing Algorithm for Information Retrieval System

The innovation of web produced a huge of information, evaluates by empowering Internet users to post their assessments, remarks, and audits on the web. Preprocessing helps to understand a user query in the Information Retrieval (IR) system. IR acts as the container to representation, seeking and access information that relates to a user search string. The information is present in natural language by using some words; it’s not structured format, and sometimes that word often ambiguous. One of the major challenges determines in current web search vocabulary mismatch problem during the preprocessing. In an IR system determine a drawback in web search; the search query string is that the relationships between the query expressions and the expanded terms are limited. The query expressions relate to search term fetching information from the IR. The expanded terms by adding those terms that is most similar to the words of the search string. In this manuscript, we mainly focus on behind user’s search string on the web. We identify the best features within this context for term selection in supervised learning based model. In this proposed system the main focus of preprocessing techniques like Tokenization, Stemming, spell check, find dissimilar words and discover the keywords from the user query because provide better results for the user

Potentials of Chatbots for Spell Check among Youngsters

Chatbots are already being used successfully in many areas. This publication deals with the development and programming of a chatbot prototype to support learning processes. This Chatbot prototype is designed to help pupils in order to correct their spelling mistakes by providing correction proposals to them. Especially orthographic spelling mistake should be recognized by the chatbot and should be replaced by correction suggestions stored in test data.

A Study of Students’ Perception on The Use Facebook Group in Improving Writing

This study attempts to know the students’ perception on a Facebook group in improving writing skill on the tenth grade of SMA 5 Kendari that focus on enhancing students writing performance level and the brainstorm ideas at the pre-writing stage. Also, to find out how Facebook influence the students’ affective domain. The researcher employed mix method design with questionnaire as instrument to collect data from 36 students. The data was analyzed by calculating the frequency distribution of Likert scale. The result reveals there was positive perception among students about applying Facebook Group in improving writing. Particularly, Respondent (89.7%) spell – check feature helps students to avoid the error spelling and (91.6) student feel motivated when they got “like” from their friends. It is suggested that future research to investigate the teacher and students’ problem in applying Facebook. In addition, the future study researcher may investigate the use Facebook Group in another skill of English. Lastly, the future study may investigation the use other SNS not only Facebook but it can be Path, Twitter, or Instagram. Keywords: Facebook Group, Perception, Writing

A New Approach to Keyboard Inputting Error Prevention and Increasing Inputting Productivity

This article describes the initial concept and developments of a new approach to reducing the number of inputting errors that are made in working with computers and decreasing the time the inputter must spend correcting errors. The approach involves intercepting and correcting errors before they are designated as such by Spell Check and eliminating the need for the time and effort of Spell Check. The operating principles and concepts of this approach, called Super ErrorCorrect™, is described, along with a software suite, that enables the implementation and the testing of the approach. This paper reports on the changes and evolution that resulted from analysis and limited Beta Testing. The preliminary data shows that not only is the Super ErrorCorrect™ approach feasible, but substantial time is saved while error rates are reduced markedly. Some data also suggests that in addition to the time saved in not using Spell Check, there is a tendency for users to type faster as they do not get negative reinforcement when they type faster and make errors as the software fixes the error in real time. Researchers are invited to collaborate in further research and licenses to the technology and software are provided at no cost if research results will be publicly disclosed.