Text Recognizer uses a machine learning

Text Recognizer uses a machine learning

API to instantly scan and extract text from images.Turn your phone into a text scanner with this app. This app allows you to perform OCR (Optical Character Recognition) on your phone and allows you to easily scan text from images. Use Text Recognizer to scan text from your images without any cost.OCR technology analyzes any image or document, and then the text scanning application compares it with all the fonts that have been entered into its database, comparing the specific characteristics of the font typical of its characters.

A few OCR-driven software

Will also handle this with a spell checker, so an image-to-text application could guess unrecognizable words. Although less accurate, the closest approximation is what is required of the software.OCR software can provide a assistance to researchers, students, in addition as companies and professionals.OCR is a powerful tool for transferring information from our analog lives to an increasingly digital world. This technology has long been used in building digital libraries, recognizing text
from natural scenes, understanding handwritten office forms. Using OCR technologies, scanned or camera-captured documents are converted into machine-editable soft copies that can be
easily edited, reproduced and transmitted.
The mobile OCR engine turns a smartphone into a document scanner and converts scanned documents from a camera or photo album into plain text and works in offline mode. Work on the general problem of text and document analysis using a camera can be categorized in many ways, by the type of device used, by application, or simply by the type of text being processed.

Various mobile OCR platforms are available

Today to convert a text image into an editable
format. Mobile OCR systems work online or offline and can be recognized handwritten and typewritten text. Online OCR
are usually cloud-based OCR platforms that send the image to their server, process the image and send back the text on the image in a computer-editable format

The main use of OCR scanner applications

Is to convert paper documents into editable software copies. Before the invention of OCR, paper documents had to be transcribed if an error was made, a laborious and time-consuming task. These text scanning aText Recognizer uses a machine learning API to instantly scan and extract text from images.plications are also finding
massive use in digitizing historical documents, automatic license plate identification, data entry,
and reading assistance for the visually impaired.

OCR Text Recognition Features:

1. OCR from images
2. History of scanned images
3. Scanned text is editable, delete, copy and share.

The OCR text recognition function supports more than 100 languages.

Language is detected automatically from your images.In recent years, several mobile OCR applications have been launched to provide quick access to text. The increasing availability of high-performance, low-cost portable digital imaging devices has created a huge opportunity to supplement traditional scanning for document imaging.

Digital cameras attached to mobile phones

PDAs or as stand-alone still or video devices are highly mobile and easy to use; they can capture images of any kind of document, including very thick books, historical pages too fragile to touch, and text in scenes; and are much more versatile than desktop scanners. Traditional scanner-based document analysis techniques give us a good reference and starting point, but cannot be applied directly to camera images. Many researchers have worked on developing mobile OCR systems. Images captured by the camera can suffer from low resolution, blur and perspective distortion, as well as complex layout and interaction of content and background. In this article, we begin by describing typical imaging devices and the imaging process, and present an overview of applications for the recognition of documents captured by digital cameras.


Handheld devices allow the user to capture images from arbitrary locations and distances that are impractical for scanners. With the recent widespread applications of computer and multimedia technologies, there is a growing demand to create a paperless environment. Automated document procurement is becoming an active area of research because much knowledge is extracted from documents such as technical reports, government files,newspapers, books, journals, magazines, letters, etc. Manually extracting knowledge from such documents can involve extensive manual work. Such manual production is time-consuming and
very laborious. most available systems work for European scripts and very few mobile apps are available for Indian script recognition. Challenges associated with complex content and layout,noisy data, and font and style variations keep the field active.

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