OCR any PSB
Drag and drop or click to select.
Private and secure
Everything happens in your browser. Your files never touch our servers.
Blazing fast
No uploading, no waiting. Convert the moment you drop a file.
Actually free
No account required. No hidden costs. No file size tricks.
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 PSB format?
Adobe Large Document Format
The PSB (Photoshop Big) image format is a file format used specifically in Adobe Photoshop for images that exceed the size limitations of the standard PSD (Photoshop Document) format. While the PSD format is widely recognized and used for its ability to preserve intricate details of digital imagery such as layers, text, masks, and color profiles, its limitation lies in its size constraint. PSD files have a maximum height and width of 30,000 pixels and a file size cap at 2GB, which, in the realm of high-resolution photography, digital painting, and detailed graphic design, can sometimes be restrictive. The PSB format was introduced to accommodate the need for larger image files, supporting dimensions up to 300,000 pixels in any direction and with a file size that can extend beyond 4 Exabytes, providing a significant leap in capacity.
To understand the importance and utility of the PSB format, it's essential to delve into the technical specifics that differentiate it from its predecessor, the PSD format. One of the core differences lies in the file structure, particularly in how image data is stored and managed. PSB employs a similar layer-based composition system, allowing users to manipulate individual components of an image without altering the underlying pixels of the other layers. However, to manage the significantly larger file sizes and increased data quantities, PSB uses a modified version of the PSD's file structure, implementing more efficient data compression methods and optimized storage algorithms designed to handle the substantial amount of information efficiently.
Another technical aspect of the PSB format is its compatibility and interoperability within the Adobe ecosystem and beyond. While inherently designed for use in Photoshop, PSB files can be shared with and opened in other Adobe applications such as Adobe Illustrator and Adobe InDesign, albeit with some limitations based on the specific application's capability to process high-resolution imagery. Interoperability extends to third-party software as well, with many popular graphic design and photo editing tools offering varying degrees of support for PSB files. This ensures that users are not confined to a single software environment and can integrate PSB files into broader creative workflows.
File integrity and preservation of data quality are paramount in digital imagery, especially when dealing with high-resolution files intended for professional use. The PSB format is designed with robust mechanisms to ensure that details such as layers, color profiles, and metadata are accurately maintained when files are saved, closed, reopened, or shared. This is achieved through meticulous data verification processes and error-checking algorithms that run during file operations, safeguarding against data corruption and loss of image fidelity. Such features are crucial for professionals who demand the highest level of precision in their work.
When discussing the implications of the PSB format for workflow efficiency, it's essential to factor in the performance considerations associated with handling large files. The increased file size can pose challenges in terms of storage requirements and system performance. Operational tasks such as opening, saving, or editing PSB files demand substantial computational resources, including RAM and CPU power. Consequently, optimizing hardware specifications and configuring software settings for performance are critical steps for professionals aiming to incorporate PSB files into their workflow seamlessly.
The advent of the PSB format also reflects a broader industry trend towards the creation and consumption of ultra-high-resolution media. As visual media's quality, detail, and dimensionality continue to rise, formats like PSB ensure that software capabilities keep pace with the demands of professionals across photography, graphic design, digital art, and related fields. This trend underscores the importance of developing efficient data handling and storage solutions that can accommodate the growing file sizes without compromising on speed or quality of access.
Security and privacy concerns are inherent in the handling of digital files, particularly those containing sensitive information or proprietary creative content. The PSB format incorporates several security features to address these concerns, including options for file encryption, access controls, and digital watermarking. These measures add layers of protection against unauthorized access, copying, or alteration of files, providing users with peace of mind about the safety of their work.
Another important aspect of the PSB format is its support for advanced imaging features and functionalities. These include support for high dynamic range (HDR) imagery, extensive depth maps, and comprehensive color management systems that are critical for achieving accurate color reproduction across different devices. Furthermore, the PSB format can store detailed 3D models and textures, making it an indispensable tool for professionals working in 3D design and animation. This wide array of supported features makes the PSB format not just a solution for large images but also a versatile tool for various complex and detailed projects.
Despite its many advantages, the PSB format does pose some challenges, particularly regarding file management and collaboration. The enormous file sizes can make sharing PSB files cumbersome, requiring the use of specialized file transfer services or cloud storage solutions that can accommodate such large data volumes. Additionally, collaborators need to have compatible software versions and sufficient hardware capabilities to view or edit PSB files, which can sometimes limit the efficiency of collaborative processes.
Looking towards the future, the continuous evolution of the PSB format will likely be shaped by advancements in technology and shifting user needs. Expectations include enhanced compression algorithms for more efficient file sizes, improvements in interoperability across a wider range of software and platforms, and innovative features that further expand the format's capabilities in high-fidelity imagery and beyond. As part of its development roadmap, Adobe and other stakeholders in the creative software industry are invested in ensuring that the PSB format remains at the forefront of digital imaging technology.
In conclusion, the PSB format represents a significant advance in the field of digital imaging, offering solutions that meet the demands of modern high-resolution projects. Its development reflects both the technical progression in image handling and the evolving needs of professionals who require the utmost in quality, versatility, and efficiency from their digital tools. While challenges exist, particularly around file size management and collaboration, ongoing innovations in technology and software design are poised to address these issues. As it stands, the PSB format is a critical asset for anyone working with large-scale digital imagery, providing a robust framework that supports the creative and technical demands of today's visual media landscape.
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
Frequently asked questions
How does this work?
This converter runs entirely in your browser. When you select a file, it is read into memory and converted to the selected format. You can then download the converted file.
How long does it take to convert a file?
Conversions start instantly, and most files are converted in under a second. Larger files may take longer.
What happens to my files?
Your files are never uploaded to our servers. They are converted in your browser, and the converted file is then downloaded. We never see your files.
What file types can I convert?
We support converting between all image formats, including JPEG, PNG, GIF, WebP, SVG, BMP, TIFF, and more.
How much does this cost?
This converter is completely free, and will always be free. Because it runs in your browser, we don't have to pay for servers, so we don't need to charge you.
Can I convert multiple files at once?
Yes! You can convert as many files as you want at once. Just select multiple files when you add them.