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Optical Character Recognition (OCR) turns images of text—scans, smartphone photos, PDFs—into machine-readable strings and, increasingly, structured data. Modern OCR is a pipeline that cleans an image, finds text, reads it, and exports rich metadata so downstream systems can search, index, or extract fields. Two widely used output standards are hOCR, an HTML microformat for text and layout, and ALTO XML, a library/archives-oriented schema; both preserve positions, reading order, and other layout cues and are supported by popular engines like Tesseract.
A quick tour of the pipeline
Preprocessing. OCR quality starts with image cleanup: grayscale conversion, denoising, thresholding (binarization), and deskewing. Canonical OpenCV tutorials cover global, adaptive and Otsu thresholding—staples for documents with nonuniform lighting or bimodal histograms. When illumination varies within a page (think phone snaps), adaptive methods often outperform a single global threshold; Otsu automatically picks a threshold by analyzing the histogram. Tilt correction is equally important: Hough-based deskewing (Hough Line Transform) paired with Otsu binarization is a common and effective recipe in production preprocessing pipelines.
Detection vs. recognition. OCR is typically split into text detection (where is the text?) and text recognition (what does it say?). In natural scenes and many scans, fully convolutional detectors like EAST efficiently predict word- or line-level quadrilaterals without heavy proposal stages and are implemented in common toolkits (e.g., OpenCV’s text detection tutorial). On complex pages (newspapers, forms, books), segmentation of lines/regions and reading order inference matter:Kraken implements traditional zone/line segmentation and neural baseline segmentation, with explicit support for different scripts and directions (LTR/RTL/vertical).
Recognition models. The classic open-source workhorse Tesseract (open-sourced by Google, with roots at HP) evolved from a character classifier into an LSTM-based sequence recognizer and can emit searchable PDFs, hOCR/ALTO-friendly outputs, and more from the CLI. Modern recognizers rely on sequence modeling without pre-segmented characters. Connectionist Temporal Classification (CTC) remains foundational, learning alignments between input feature sequences and output label strings; it’s widely used in handwriting and scene-text pipelines.
In the last few years, Transformers reshaped OCR. TrOCR uses a vision Transformer encoder plus a text Transformer decoder, trained on large synthetic corpora then fine-tuned on real data, with strong performance across printed, handwritten and scene-text benchmarks (see also Hugging Face docs). In parallel, some systems sidestep OCR for downstream understanding: Donut (Document Understanding Transformer) is an OCR-free encoder-decoder that directly outputs structured answers (like key-value JSON) from document images (repo, model card), avoiding error accumulation when a separate OCR step feeds an IE system.
Engines and libraries
If you want batteries-included text reading across many scripts, EasyOCR offers a simple API with 80+ language models, returning boxes, text, and confidences—handy for prototypes and non-Latin scripts. For historical documents, Kraken shines with baseline segmentation and script-aware reading order; for flexible line-level training, Calamari builds on the Ocropy lineage (Ocropy) with (multi-)LSTM+CTC recognizers and a CLI for fine-tuning custom models.
Datasets and benchmarks
Generalization hinges on data. For handwriting, the IAM Handwriting Database provides writer-diverse English sentences for training and evaluation; it’s a long-standing reference set for line and word recognition. For scene text, COCO-Text layered extensive annotations over MS-COCO, with labels for printed/handwritten, legible/illegible, script, and full transcriptions (see also the original project page). The field also relies heavily on synthetic pretraining: SynthText in the Wild renders text into photographs with realistic geometry and lighting, providing huge volumes of data to pretrain detectors and recognizers (reference code & data).
Competitions under ICDAR’s Robust Reading umbrella keep evaluation grounded. Recent tasks emphasize end-to-end detection/reading and include linking words into phrases, with official code reporting precision/recall/F-score, intersection-over-union (IoU), and character-level edit-distance metrics—mirroring what practitioners should track.
Output formats and downstream use
OCR rarely ends at plain text. Archives and digital libraries prefer ALTO XML because it encodes the physical layout (blocks/lines/words with coordinates) alongside content, and it pairs well with METS packaging. The hOCR microformat, by contrast, embeds the same idea into HTML/CSS using classes like ocr_line and ocrx_word, making it easy to display, edit, and transform with web tooling. Tesseract exposes both—e.g., generating hOCR or searchable PDFs directly from the CLI (PDF output guide); Python wrappers like pytesseract add convenience. Converters exist to translate between hOCR and ALTO when repositories have fixed ingestion standards—see this curated list of OCR file-format tools.
Practical guidance
- Start with data & cleanliness. If your images are phone photos or mixed-quality scans, invest in thresholding (adaptive & Otsu) and deskew (Hough) before any model tuning. You’ll often gain more from a robust preprocessing recipe than from swapping recognizers.
- Choose the right detector. For scanned pages with regular columns, a page segmenter (zones → lines) may suffice; for natural images, single-shot detectors like EAST are strong baselines and plug into many toolkits (OpenCV example).
- Pick a recognizer that matches your text. For printed Latin, Tesseract (LSTM/OEM) is sturdy and fast; for multi-script or quick prototypes, EasyOCR is productive; for handwriting or historical typefaces, consider Kraken or Calamari and plan to fine-tune. If you need tight coupling to document understanding (key-value extraction, VQA), evaluate TrOCR (OCR) versus Donut (OCR-free) on your schema—Donut may remove a whole integration step.
- Measure what matters. For end-to-end systems, report detection F-score and recognition CER/WER (both based on Levenshtein edit distance; see CTC); for layout-heavy tasks, track IoU/tightness and character-level normalized edit distance as in ICDAR RRC evaluation kits.
- Export rich outputs. Prefer hOCR /ALTO (or both) so you keep coordinates and reading order—vital for search hit highlighting, table/field extraction, and provenance. Tesseract’s CLI and pytesseract make this a one-liner.
Looking ahead
The strongest trend is convergence: detection, recognition, language modeling, and even task-specific decoding are merging into unified Transformer stacks. Pretraining on large synthetic corpora remains a force multiplier. OCR-free models will compete aggressively wherever the target is structured outputs rather than verbatim transcripts. Expect hybrid deployments too: a lightweight detector plus a TrOCR-style recognizer for long-form text, and a Donut-style model for forms and receipts.
Further reading & tools
Tesseract (GitHub) · Tesseract docs · hOCR spec · ALTO background · EAST detector · OpenCV text detection · TrOCR · Donut · COCO-Text · SynthText · Kraken · Calamari OCR · ICDAR RRC · pytesseract · IAM handwriting · OCR file-format tools · EasyOCR
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 PSD format?
Adobe Photoshop bitmap
The PSD format, standing for Photoshop Document, is a proprietary file type developed by Adobe Inc. for its widely used Photoshop software. Since its inception, it has become a staple in the digital art and graphic design industries, renowned for its flexibility and comprehensive support for various image editing techniques. The format is specifically engineered to store an image’s full editing history, including layers, masks, colors, and even historical states, providing a non-destructive editing workflow. This enables artists and designers to revisit and modify any aspect of their project without losing the original data.
One of the hallmarks of the PSD format is its layered structure. Unlike traditional image formats that flatten all elements into a single layer, PSD files maintain each element as a separate layer. This could range from text, shapes, adjustments layers, to more complex elements like smart objects and layer effects. This layered approach not only allows for more sophisticated design and editing strategies but also facilitates a more organized and efficient workflow. Users can independently manipulate elements, adjust their visibility, and re-order them without affecting the rest of the image.
Alongside layers, PSD files also support transparency, which is crucial for composing images with variable visibility and creating graphics with intricate cutouts. Transparency in PSD files is managed through alpha channels, which store information about the opacity of different parts of the image. This feature is indispensable for adding depth and complexity to visuals, making the format highly favored for tasks requiring precision and detailed manipulation, such as web design, animation, and special effects in video production.
Another significant advantage of the PSD format is its support for sophisticated text editing. When text is added to a PSD file, it remains fully editable, allowing users to modify font properties, alignment, color, and effect without rasterizing the text or converting it into an image layer. This is particularly valuable for design work that requires frequent text adjustments, as it preserves the text’s crispness and clarity regardless of how many times it is edited. Furthermore, Photoshop’s advanced text functionalities, such as text on a path or shape, and the ability to import and export text for use in other applications, make PSD files extremely versatile for projects involving intricate typography.
PSD files are also known for their extensive compatibility with a wide range of color models and depth. They support everything from grayscale to multichannel color modes including RGB, CMYK, and Lab color. This makes them highly adaptable for various uses, from digital design viewable on screens to print-ready projects requiring CMYK color specification. Additionally, PSD files can store an impressive color depth of up to 32 bits per channel, providing a high dynamic range and allowing for more precise color correction and grading techniques.
The ability to include adjustment layers is another feature that sets the PSD format apart. These layers contain settings for color correction, exposure, contrast, and other enhancements that can be applied to underlying layers without permanently altering the original image data. This means adjustments can be tweaked or removed at any stage of the editing process, offering unparalleled flexibility. Adjustment layers work hand in hand with layer masks, which enable selective application of effects, further accentuating the non-destructive ethos of the PSD format.
PSD files also support the inclusion of vector elements, such as shapes and text, which remain perfectly scalable without loss of quality. This is due to the mathematical nature of vector graphics, which are resolution-independent. The integration of vector technology into a predominantly raster-based format like PSD allows for a harmony between scalability and detailed editing. This combination is crucial for applications where both clarity at any size and pixel-level detail are required, such as logo design, web graphics, and scalable compositions.
The inclusion of Smart Objects in PSD files marks another leap in sophisticated image editing. Smart Objects preserve an image's source content with all its original characteristics, allowing for non-destructive scaling, rotation, and warping. They can also be linked to external files, ensuring that when the external file is updated, the PSD file reflects these updates automatically. This feature is particularly useful for collaborative workflows and for projects that involve repetitive elements that may need to be updated across multiple files.
Photoshop's automation features are closely tied to the PSD format. Actions, which are sequences of tasks recorded by the user, can be saved within PSD files for repetitive processing, significantly speeding up the workflow for tasks such as resizing, formatting, or applying filters across multiple files. Similarly, Photoshop scripts, which are more complex and capable of conditional logic and sophisticated processing, can also be applied to PSD files, further extending the software's capabilities in automating routine tasks and complex procedures.
Despite its numerous advantages, the PSD format's rich feature set comes with the trade-off of file size. PSD files often occupy significant storage space, especially when saving large images with multiple layers, high color depth, and additional features like Smart Objects. This can be mitigated to some extent by using features like layer compression and maximizing the use of adjustment layers instead of duplicating content. However, for long-term storage or sharing, many users resort to flattening images or saving copies in more size-efficient formats like JPEG or PNG for distribution, while keeping the original PSD for editing purposes.
Interoperability is one of the strong suits of the PSD format. Despite being proprietary to Adobe, PSD files can be opened and, to a varying degree, edited in a plethora of third-party software applications. This is thanks to Adobe's documentation of the format and the efforts within the software development community to maintain compatibility. However, not all applications support the full range of PSD features, and users may find that some elements like layer effects and adjustment layers do not translate perfectly across different software, necessitating some caution when moving files between applications.
Adobe has introduced the PSB (Photoshop Big) format as an extension of PSD to cater to modern demands for extremely large images. PSB supports an essentially unlimited file size, accommodating documents up to 300,000 pixels in any dimension, as opposed to the 30,000 pixel limit of PSD files. This is particularly useful for high resolution photography, large-scale composite images, and detailed digital paintings. Despite these differences, PSB maintains compatibility with most of the features available in PSD files, offering a seamless workflow for projects that exceed the PSD format's limits.
In conclusion, the PSD image format is an intricate and versatile file type designed to cater to the needs of the digital art and graphic design communities. Its support for non-destructive editing, layered compositions, transparency, extensive color models, adjustment layers, vector elements, and smart objects make it an indispensable tool in professional workflows. While its complexities and file size can pose challenges, the benefits it offers in terms of flexibility and quality are unrivaled. The ongoing development and compatibility efforts surrounding the PSD format ensure that it remains central to creative professions, underpinning a wide range of projects from simple designs to complex digital art pieces.
Supported formats
AAI.aai
AAI Dune image
AI.ai
Adobe Illustrator CS2
AVIF.avif
AV1 Image File Format
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
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
FF.ff
Farbfeld
FITS.fits
Flexible Image Transport System
GIF.gif
CompuServe graphics interchange format
HDR.hdr
High Dynamic Range image
HEIC.heic
High Efficiency Image Container
HRZ.hrz
Slow Scan TeleVision
ICO.ico
Microsoft icon
ICON.icon
Microsoft icon
J2C.j2c
JPEG-2000 codestream
J2K.j2k
JPEG-2000 codestream
JNG.jng
JPEG Network Graphics
JP2.jp2
JPEG-2000 File Format Syntax
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
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
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
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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