OCR any YUV

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OCR, or Optical Character Recognition, is a technology used to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera, into editable and searchable data.

In the first stage of OCR, an image of a text document is scanned. This could be a photo or a scanned document. The purpose of this stage is to make a digital copy of the document, instead of requiring manual transcription. Additionally, this digitization process can also help increase the longevity of materials because it can reduce the handling of fragile resources.

Once the document is digitized, the OCR software separates the image into individual characters for recognition. This is called the segmentation process. Segmentation breaks down the document into lines, words, and then ultimately individual characters. This division is a complex process because of the myriad factors involved -- different fonts, different sizes of text, and varying alignment of the text, just to name a few.

After segmentation, the OCR algorithm then uses pattern recognition to identify each individual character. For each character, the algorithm will compare it to a database of character shapes. The closest match is then selected as the character's identity. In feature recognition, a more advanced form of OCR, the algorithm not only examines the shape but also takes into account lines and curves in a pattern.

OCR has numerous practical applications -- from digitizing printed documents, enabling text-to-speech services, automating data entry processes, to even assisting visually impaired users to better interact with text. However, it is worth noting that the OCR process isn't infallible and may make mistakes especially when dealing with low-resolution documents, complex fonts, or poorly printed texts. Hence, accuracy of OCR systems varies significantly depending upon the quality of the original document and the specifics of the OCR software being used.

OCR is a pivotal technology in modern data extraction and digitization practices. It saves significant time and resources by mitigating the need for manual data entry and providing a reliable, efficient approach to transforming physical documents into a digital format.

Frequently Asked Questions

What is OCR?

Optical Character Recognition (OCR) is a technology used to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera, into editable and searchable data.

How does OCR work?

OCR works by scanning an input image or document, segmenting the image into individual characters, and comparing each character with a database of character shapes using pattern recognition or feature recognition.

What are some practical applications of OCR?

OCR is used in a variety of sectors and applications, including digitizing printed documents, enabling text-to-speech services, automating data entry processes, and assisting visually impaired users to better interact with text.

Is OCR always 100% accurate?

While great advancements have been made in OCR technology, it isn't infallible. Accuracy can vary depending upon the quality of the original document and the specifics of the OCR software being used.

Can OCR recognize handwriting?

Although OCR is primarily designed for printed text, some advanced OCR systems are also able to recognize clear, consistent handwriting. However, typically handwriting recognition is less accurate because of the wide variation in individual writing styles.

Can OCR handle multiple languages?

Yes, many OCR software systems can recognize multiple languages. However, it's important to ensure that the specific language is supported by the software you're using.

What's the difference between OCR and ICR?

OCR stands for Optical Character Recognition and is used for recognizing printed text, while ICR, or Intelligent Character Recognition, is more advanced and is used for recognizing hand-written text.

Does OCR work with any font and text size?

OCR works best with clear, easy-to-read fonts and standard text sizes. While it can work with various fonts and sizes, accuracy tends to decrease when dealing with unusual fonts or very small text sizes.

What are the limitations of OCR technology?

OCR can struggle with low-resolution documents, complex fonts, poorly printed texts, handwriting, and documents with backgrounds that interfere with the text. Also, while it can work with many languages, it may not cover every language perfectly.

Can OCR scan colored text or colored backgrounds?

Yes, OCR can scan colored text and backgrounds, although it's generally more effective with high-contrast color combinations, such as black text on a white background. The accuracy might decrease when text and background colors lack sufficient contrast.

What is the YUV format?

CCIR 601 4:1:1 or 4:2:2

YUV is a color encoding system used as a part of a color image pipeline. It encodes a color image or video taking human perception into account, allowing reduced bandwidth for chrominance components, thereby typically enabling transmission errors or compression artifacts to be more efficiently masked by the human perception than using a "direct" RGB-representation. The name YUV itself is derived from the Y'UV notation originally used for the luma (Y') and two chrominance (UV) components. The Y'UV model defines a color space in terms of one luma component (Y') and two chrominance components, called U (blue projection) and V (red projection), while YCbCr is a digital version of the Y'UV color model.

YUV signals are created from an original RGB (red, green and blue) source. The weighted values of R, G and B are added together to produce a single Y signal, representing the overall brightness, or luma, of that pixel. The U signal is then created by subtracting the Y from the blue signal of the original RGB, and then scaling; and V by subtracting the Y from the red, and then scaling by a different factor. These factors are chosen to make sure the range of each color space coordinate is roughly -0.5 to +0.5.

The transformation RGB→YUV is specified as follows: Y = 0.299R + 0.587G + 0.114B, U = −0.147R − 0.289G + 0.436B, V = 0.615R − 0.515G − 0.100B. Digital formats commonly use 8 bits for each channel, making the range for each 0 to 255, and so the transform becomes: Y = (0.257 × R) + (0.504 × G) + (0.098 × B) + 16, Cb = U = −(0.148 × R) − (0.291 × G) + (0.439 × B) + 128, Cr = V = (0.439 × R) − (0.368 × G) − (0.071 × B) + 128.

The YUV color model is used in the PAL, NTSC, and SECAM composite color video standards. The luma component is often denoted as Y', but sometimes as Y, prime symbols are often omitted in writing. The YUV system allows the transmission of color images over a channel intended for black-and-white (luma) signals, reducing the bandwidth needed. The black-and-white receivers still display a normal black-and-white picture, while color receivers reverse the process, decoding the UV portions of the signal and displaying a color picture.

One major advantage of YUV is that some of the information may be discarded in order to reduce bandwidth or when chroma is to be processed separately from luma. If only luma needs to be transmitted, that is, the U and V components are zero throughout the frame, then the data size is half of what it was before with no loss to perceived image quality. When converting from full color to YUV and back again, there is some loss of information due to rounding errors.

YUV subsampling is a method of encoding images by implementing less resolution for chroma information than for luma information, taking advantage of the human visual system's lower acuity for color differences than for luminance. 4:4:4 full-resolution YUV stores no chroma subsampling, while common schemes are 4:2:2 (half resolution horizontally), 4:2:0 (half resolution horizontally and vertically) and 4:1:1 (one quarter resolution horizontally). 4:4:4 subsampling preserves all the information present in the original sample. The ratios describe how many luma and chroma samples are encoded for a block of pixels.

There are several shades of YUV color spaces used in video and digital photography systems. The main differences are the scale factors for the U and V planes in the basic equations. While the Y plane represents luminance, and thus requires higher bandwidth, the U and V planes can be bandwidth-reduced, subsampled, compressed, or otherwise treated separately for improved system efficiency. Thus there are several YUV formats, possibly using shades of 8-bit or 10-bit encoding for the planes.

The YUV color model has seen widespread use in digital video, including use in television standards like PAL, NTSC and SECAM, in MPEG compression, in modern digital video interfaces like HDMI, digital video compression schemes like H.264 and VP9, and common image/video container formats such as JPEG/JFIF, PNG and WebP. Its popularity is due to its usefulness in color compression and its ability to take advantage of human perception for more efficient storage and transmission. Overall, YUV remains one of the most important and widely used color models in digital imaging and video.

Supported formats

AAI.aai

AAI Dune image

AI.ai

Adobe Illustrator CS2

AVIF.avif

AV1 Image File Format

AVS.avs

AVS X image

BAYER.bayer

Raw Bayer Image

BMP.bmp

Microsoft Windows bitmap image

CIN.cin

Cineon Image File

CLIP.clip

Image Clip Mask

CMYK.cmyk

Raw cyan, magenta, yellow, and black samples

CMYKA.cmyka

Raw cyan, magenta, yellow, black, and alpha samples

CUR.cur

Microsoft icon

DCX.dcx

ZSoft IBM PC multi-page Paintbrush

DDS.dds

Microsoft DirectDraw Surface

DPX.dpx

SMTPE 268M-2003 (DPX 2.0) image

DXT1.dxt1

Microsoft DirectDraw Surface

EPDF.epdf

Encapsulated Portable Document Format

EPI.epi

Adobe Encapsulated PostScript Interchange format

EPS.eps

Adobe Encapsulated PostScript

EPSF.epsf

Adobe Encapsulated PostScript

EPSI.epsi

Adobe Encapsulated PostScript Interchange format

EPT.ept

Encapsulated PostScript with TIFF preview

EPT2.ept2

Encapsulated PostScript Level II with TIFF preview

EXR.exr

High dynamic-range (HDR) image

FARBFELD.ff

Farbfeld

FF.ff

Farbfeld

FITS.fits

Flexible Image Transport System

GIF.gif

CompuServe graphics interchange format

GIF87.gif87

CompuServe graphics interchange format (version 87a)

GROUP4.group4

Raw CCITT Group4

HDR.hdr

High Dynamic Range image

HRZ.hrz

Slow Scan TeleVision

ICO.ico

Microsoft icon

ICON.icon

Microsoft icon

IPL.ipl

IP2 Location Image

J2C.j2c

JPEG-2000 codestream

J2K.j2k

JPEG-2000 codestream

JNG.jng

JPEG Network Graphics

JP2.jp2

JPEG-2000 File Format Syntax

JPC.jpc

JPEG-2000 codestream

JPE.jpe

Joint Photographic Experts Group JFIF format

JPEG.jpeg

Joint Photographic Experts Group JFIF format

JPG.jpg

Joint Photographic Experts Group JFIF format

JPM.jpm

JPEG-2000 File Format Syntax

JPS.jps

Joint Photographic Experts Group JPS format

JPT.jpt

JPEG-2000 File Format Syntax

JXL.jxl

JPEG XL image

MAP.map

Multi-resolution Seamless Image Database (MrSID)

MAT.mat

MATLAB level 5 image format

PAL.pal

Palm pixmap

PALM.palm

Palm pixmap

PAM.pam

Common 2-dimensional bitmap format

PBM.pbm

Portable bitmap format (black and white)

PCD.pcd

Photo CD

PCDS.pcds

Photo CD

PCT.pct

Apple Macintosh QuickDraw/PICT

PCX.pcx

ZSoft IBM PC Paintbrush

PDB.pdb

Palm Database ImageViewer Format

PDF.pdf

Portable Document Format

PDFA.pdfa

Portable Document Archive Format

PFM.pfm

Portable float format

PGM.pgm

Portable graymap format (gray scale)

PGX.pgx

JPEG 2000 uncompressed format

PICON.picon

Personal Icon

PICT.pict

Apple Macintosh QuickDraw/PICT

PJPEG.pjpeg

Joint Photographic Experts Group JFIF format

PNG.png

Portable Network Graphics

PNG00.png00

PNG inheriting bit-depth, color-type from original image

PNG24.png24

Opaque or binary transparent 24-bit RGB (zlib 1.2.11)

PNG32.png32

Opaque or binary transparent 32-bit RGBA

PNG48.png48

Opaque or binary transparent 48-bit RGB

PNG64.png64

Opaque or binary transparent 64-bit RGBA

PNG8.png8

Opaque or binary transparent 8-bit indexed

PNM.pnm

Portable anymap

PPM.ppm

Portable pixmap format (color)

PS.ps

Adobe PostScript file

PSB.psb

Adobe Large Document Format

PSD.psd

Adobe Photoshop bitmap

RGB.rgb

Raw red, green, and blue samples

RGBA.rgba

Raw red, green, blue, and alpha samples

RGBO.rgbo

Raw red, green, blue, and opacity samples

SIX.six

DEC SIXEL Graphics Format

SUN.sun

Sun Rasterfile

SVG.svg

Scalable Vector Graphics

SVGZ.svgz

Compressed Scalable Vector Graphics

TIFF.tiff

Tagged Image File Format

VDA.vda

Truevision Targa image

VIPS.vips

VIPS image

WBMP.wbmp

Wireless Bitmap (level 0) image

WEBP.webp

WebP Image Format

YUV.yuv

CCIR 601 4:1:1 or 4:2:2

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