OCR any DCX
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 DCX format?
ZSoft IBM PC multi-page Paintbrush
The DCX image format, designated as an extension .dcx, is a noteworthy graphical file format that primarily serves the purpose of encapsulating multiple PCX format images in a single file. This functionality makes it particularly useful for applications requiring the organization, storage, and transportation of image sequences or documents with numerous pages, such as fax documents, animated images, or multi-page documents. Developed during the early days of personal computing, the DCX format stands as a testament to the evolving needs of digital imagery management, providing a solution for bulk image handling.
The PCX format, which forms the foundation of DCX, was one of the earliest bitmap image formats widely adopted in the software industry, primarily by the PC Paintbrush software. As a raster image format, it encoded individual pixel information within a file, supporting various color depths and effectively serving as the groundwork for the composite DCX format. Despite its age, PCX—and by extension, DCX—remains in use within certain niches due to its simplicity and compatibility with older software applications.
The structure of a DCX file is essentially a header followed by a series of PCX files. The header part of the DCX file starts with a unique identifier ('0x3ADE68B1'), which serves as a magic number to distinguish DCX files from other file formats confidently. Following the magic number, there is a directory that lists the offset positions of each encapsulated PCX image within the DCX file. This approach enables quick access to individual images without the need to sequentially parse the entire file, enhancing the format’s efficiency for accessing specific content.
Each entry in the directory section consists of a 32-bit offset pointing to the start of a PCX image within the DCX file. The simplicity of this directory structure allows for the swift addition, removal, or replacement of PCX images in a DCX file without extensive file reprocessing. It highlights the format's design foresight in enabling manageable updating and editing of multi-page document images or sequential image collections.
In terms of technical encoding, a PCX file encapsulated within a DCX container stores its image data as a series of scanlines. These scanlines are compressed using run-length encoding (RLE), a form of lossless data compression that reduces file size without compromising the original image quality. RLE is particularly efficient for images with large areas of uniform color, making it well-suited for the scanned document images and simple graphics typically associated with the PCX and DCX formats.
The flexibility of the PCX format regarding color depth plays a significant role in the adaptability of the DCX format. PCX files can handle monochrome, 16-color, 256-color, and true color (24-bit) images, allowing DCX containers to encapsulate a wide range of image types. This versatility ensures the DCX format's continued relevance for archival purposes, where preserving the fidelity of original documents or images is paramount.
Despite its advantages, the DCX format faces limitations intrinsic to its design and the technology era it originates from. For one, the format does not inherently support advanced image features like layers, transparency, or metadata, which have become standard in more modern image file formats. These limitations reflect the format's utility in more straightforward applications, such as document scanning and archiving, rather than complex image editing or digital artwork creation.
Additionally, while the run-length encoding method employed by the PCX and hence DCX formats is efficient for certain types of images, it may not provide the most optimal compression for all scenarios. Modern image compression algorithms, such as those used in JPEG or PNG formats, offer more sophisticated methods, achieving higher compression ratios and better quality at smaller file sizes for a wider range of images. However, the simplicity of RLE and the absence of lossy compression artifacts in DCX images ensure that they maintain their original visual integrity without degradation.
Furthermore, the reliance on the PCX format within DCX files also means inheriting the limitations and challenges associated with PCX. For instance, handling modern high-resolution images or those with a wide color gamut can be problematic, given the color depth restrictions and the inefficiency of RLE compression for complex images. Consequently, while DCX files excel in storing simpler images or document scans efficiently, they may not be the ideal choice for high-quality photography or detailed graphic work.
From a software compatibility perspective, the DCX format enjoys support from a range of image viewing and editing programs, particularly those designed to work with legacy file formats or specialized in document imaging. This interoperability ensures that users can access and manipulate DCX files without significant hurdles, leveraging existing software solutions. Nevertheless, as the digital imaging landscape evolves, the prevalence of more advanced and flexible image formats poses a challenge to the continued adoption and support of DCX, potentially relegating it to more niche or legacy applications.
In light of these considerations, the future of the DCX format appears to be closely tied to its niche applications, where its specific advantages—such as the efficient storage of multi-page document images in a single file and the preservation of original image quality through lossless compression—outweigh its limitations. Industries and applications that prioritize these factors, such as legal document archiving, historical document preservation, and certain types of technical documentation, may continue to find value in the DCX format.
Moreover, the DCX format's role in preserving digital legacy and historical documents cannot be understated. In contexts where maintaining the authenticity and integrity of original documents is crucial, the simplicity and reliability of the DCX format may offer advantages over more complex formats that require modern computing resources. The format's emphasis on lossless compression and support for a range of color depths ensures that digital reproductions closely match the original documents, an essential consideration for archival purposes.
Given these strengths and weaknesses, the DCX format's relevance in contemporary digital imaging hinges on its continued utility in specific use cases rather than broad mainstream adoption. While it may not compete with modern image formats in terms of features or efficiency across all scenarios, DCX holds a niche but significant place in the digital imaging ecosystem, particularly in legacy systems and specific industries where its unique capabilities are most valued.
To sum up, the DCX image format exemplifies the balance between simplicity, efficiency, and functionality in managing multi-page image documents or sequences. Its reliance on the venerable PCX format grounds it in a legacy of early digital image management while also delineating its capabilities and limitations. Despite facing challenges in the face of more advanced and versatile image formats, DCX retains its relevance in specific applications where its attributes—such as lossless compression, efficient handling of multiple images, and compatibility with older software—align with the practical needs of users and industries.
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.