Also you get the HEX color code value, RGB value and HSV value. The transformation in a configuration of two entirely different documents is now made simple with our JPG to Text format conversion tool.Text Extraction From Image and Text to Speech ConversionOCR Convert is an online OCR service that allows you to convert scanned images to editable text formats - Allows you to convert PDF to Text, Image to Text, PDF to Word and much more.Use the online image color picker above to select a color and get the HTML Color Code of this pixel. This conversion can be done easily by using our вЂJPG to Txt FormatвЂ™ converter. ItвЂ™s an online tool that converts your images into the plain text format. What is JPG to Text Converter.

Multi-page documents: pdf. Select the language of the text content in the file. Or use an website url, you will see a Xavier Institute Of Engineering Mumbai University Mumbai,IndiaScan text or get a photo for OCR recognition. (for example an screenshot of your desktop).

All files will be zipped for downloading.Abstract The recent technological advancements in the field of Image Processing and Natural Language Processing are focusing on developing smart systems to improve the quality of life. The plain text from each image will be written in a. You can add 5 pictures each time for the image OCR. All uploaded images will be cleared every 30 minutes.

Finally, Optical Character Recognition (OCR) is employed to digitize the words. Subsequently, individual letter/alphabets are grouped to detect text sequences, which are then fragmented into words. The non-text MSERs are removed by using geometric properties and stroke width transform. The next step is to apply geometric filtering in combination with stroke width transform (SWT) to efficiently collect and filter text regions in an image. Afterwards, the text regions of the enhanced image are detected by employing the Maximally Stable External Regions (MSER) feature detector. The incoming image is firstly enhanced by employing gray scale conversion.

Text extraction and TTS can be utilized together to help people with reading disabilities and visual impairment to listen to written information by a computer system. It is done in order to read the information aloud for the benefit and ease of the user. Therefore, the scientists have started to digitize these images, extract and interpret the data by using specific techniques, and then perform text-to-speech synthesis (TTS). In the recent era, visual text in natural or manmade scenes might carry very important and useful information. TTS (text-to-speech synthesizer).Languages are the oldest way of communication between human beings whether they are in spoken or written forms.

Promising results have been reported which prove the accuracy and robustness of the proposed algorithm and encourage its practical implementation in real world scenarios.Rest of the paper is structured as follows: Section II covers the background of the research problem addressed in this paper and related methods. The proposed algorithm is tested on images representing different scenes ranging from documents to natural scenes. Optical character recognition is employed to recognize and extract the words and finally the extracted text is converted to appropriate speech using text-to-speech synthesizer. Afterwards, the selected objects are grouped into detected text sequences, which are then fragmented into isolated words. CCs that are likely to be characters are selected on the basis of their geometric properties andStroke width variation.

Also the complex background and bad quality of the images can hamper the performance of texture based approach.In this work, we propose a robust MSER method to extract the text from images. Although texture based method seems to be a better classification method of text/non text regions but the areComputationally demanding and sensitive to text alignment. Whereas texture-based approaches follow top-down approach since they are based on the textual properties of the text such as statistical features, Gabor filters, frequency transform and many other. Region-based method is a bottom-up approach where we detect small candidate regions and then group them into text regions. Region- based method can further be subdivided into various categories such as edge-based, color-based, stroke-based and many other. The methods can be broadly classified into two main types those are region-based approach and texture-based approach.In region-based approach the properties of the textual region which distinguish them from the background are taken into consideration such as color, intensity, edges ,etc.

MSER is compatible with text due to the constant color and high contrast with the background, which together give us stable intensity profiles. Once the text regions are detected, the other non-text regions are removed. Once the MSER regions are detected those region are further processed using geometric properties, connected components and stroke width variation. Segmenting the text from a scene via MSER intensively helps in further processing of image for detecting text regions. The regions that preserve constant shapes over a wide range of thresholds are selected. They are retrieved through a process of attempting numerous thresholds.

To remove the non-text regions the stroke-width is considered. Connected components within a region are also considered for detecting the region of interest. Geometric properties such as eccentricity, bounding box ,solidity, euler number are also taken into consideration for detection of text regions.

Finally the detected text is converted to speech using text-to-speech synthesizer. The detected text regions undergo OCR (Optical Character Recognition) for digitizing the text regions and to detect and extract the text from image. So the regions that have high stroke width variation are removed as they more likely to be non-text regions.

The pixel value of a grayscale image ranges from 0 to 255. Architecture of proposed methodThe various components of the proposed system are as followsThe grayscale image is represented by using 8 bits value. A given threshold is minimal.Determining Bounding Boxes Enclosing textFigure 2. Points in a blob are similar.Boundary: Boundary of a region is the group of pixels neighboring at least one pixel of that region but not a part of that region.Extremal Region: If all the pixels in a region have values greater than (or smaller than) that of the boundary, the region is called extremal region.Maximally Stable Extremal Region (MSER): An extremal region is termed as maximally stable when its variation w.r.t.

The value should be between 0 and 1. It contains all the pixel values within the enclosing boundary.Eccentricity: The eccentricity is the ratio of the distance between its major axis length and the foci. The pixels in the connected components are connected to each other through either 4-pixel, or 8-pixel connectivity.The following geometric properties are taken into consideration:Bounding Box: Bounding boxes are rectangular boxes created around the region of interest. The selected regions are those that maintain unchanged over a large set of thresholds.Connected components of an image are the regions which have continuous pixels within that region. MSER regions are connected areas characterized by almost uniform intensity throughout a range of thresholds. One method of converting RGB to grayscale is to take the average of the contribution from each pixel (R+G+B)/3.Maximally stable extremal regions are used as a method of blob detection in images.

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