Open Source OCR & Documents

Turn scans, PDFs and images into usable text. Optical character recognition, layout analysis, table extraction and document understanding.

693
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1.4M โ˜…
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+6.4K
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ranked by GitHub stars ยท updated Nov 12, 2026

๐Ÿ”ฅ Trending this week in OCR & Documents

OCR & Documents

yjid/kensaku๐Ÿ”ฅ active

โ–ฒ +877 โ˜… this week

Search inside documents, code, and even screenshots - instantly and locally. A free, open-source file launcher with OCR, semantic search, and MCP agent access.

๐Ÿ” Search & Retrieval
RustMITupdated Nov 10, 2026
GitHub โ†—โ˜… 1.7Kโ‘‚ 112
OCR & Documents

SoMarkAI/OXR๐Ÿ”ฅ active

โ–ฒ +406 โ˜… this week

An open-source document parsing model with SOTA accuracy and speed, using an RSI loop during post-training.

PythonOtherupdated Nov 6, 2026
GitHub โ†—โ˜… 3.8Kโ‘‚ 255
OCR & Documents

opendatalab/MinerU๐Ÿ”ฅ active

โ–ฒ +385 โ˜… this week

Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.

PythonOtherupdated Nov 12, 2026
GitHub โ†—โ˜… 83.6Kโ‘‚ 6.9K
OCR & Documents

ShareX/ShareX๐Ÿ”ฅ active

โ–ฒ +343 โ˜… this week

ShareX is a free and open-source application that enables users to capture or record any area of their screen with a single keystroke. It also supports uploading images, text, and various file types to a wide range of destinations.

C#GPL-3.0updated Nov 12, 2026
GitHub โ†—โ˜… 41Kโ‘‚ 4.1K
OCR & Documents

paperless-ngx/paperless-ngx๐Ÿ”ฅ active

โ–ฒ +329 โ˜… this week

A community-supported supercharged document management system: scan, index and archive all your documents

PythonGPL-3.0updated Nov 12, 2026
GitHub โ†—โ˜… 48.1Kโ‘‚ 3.4K
OCR & Documents

PaddlePaddle/PaddleOCR

โ–ฒ +327 โ˜… this week

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

๐Ÿ” Search & Retrieval
PythonApache-2.0updated Sep 16, 2026
GitHub โ†—โ˜… 92.6Kโ‘‚ 11.7K

Top 30 OCR and document parsing tools by stars

OCR & Documents๐Ÿฅ‡#1

PaddlePaddle/PaddleOCR

โ–ฒ +327 โ˜… this week

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

๐Ÿ” Search & Retrieval
PythonApache-2.0updated Sep 16, 2026
GitHub โ†—โ˜… 92.6Kโ‘‚ 11.7K
OCR & Documents๐Ÿฅˆ#2

opendatalab/MinerU๐Ÿ”ฅ active

โ–ฒ +385 โ˜… this week

Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.

PythonOtherupdated Nov 12, 2026
GitHub โ†—โ˜… 83.6Kโ‘‚ 6.9K
OCR & Documents๐Ÿฅ‰#3

tesseract-ocr/tesseract๐Ÿ”ฅ active

โ–ฒ +119 โ˜… this week

Tesseract Open Source OCR Engine (main repository)

C++Apache-2.0updated Nov 11, 2026
GitHub โ†—โ˜… 77.5Kโ‘‚ 10.9K
OCR & Documents#5

hiroi-sora/Umi-OCR

โ–ฒ +161 โ˜… this week

OCR software, free and offline. ๅผ€ๆบใ€ๅ…่ดน็š„็ฆป็บฟOCR่ฝฏไปถใ€‚ๆ”ฏๆŒๆˆชๅฑ/ๆ‰น้‡ๅฏผๅ…ฅๅ›พ็‰‡๏ผŒPDFๆ–‡ๆกฃ่ฏ†ๅˆซ๏ผŒๆŽ’้™คๆฐดๅฐ/้กต็œ‰้กต่„š๏ผŒๆ‰ซๆ/็”ŸๆˆไบŒ็ปด็ ใ€‚ๅ†…็ฝฎๅคšๅ›ฝ่ฏญ่จ€ๅบ“ใ€‚

PythonMITupdated Nov 20, 2025
GitHub โ†—โ˜… 48.6Kโ‘‚ 4.7K
OCR & Documents#6

paperless-ngx/paperless-ngx๐Ÿ”ฅ active

โ–ฒ +329 โ˜… this week

A community-supported supercharged document management system: scan, index and archive all your documents

PythonGPL-3.0updated Nov 12, 2026
GitHub โ†—โ˜… 48.1Kโ‘‚ 3.4K
OCR & Documents#7

ShareX/ShareX๐Ÿ”ฅ active

โ–ฒ +343 โ˜… this week

ShareX is a free and open-source application that enables users to capture or record any area of their screen with a single keystroke. It also supports uploading images, text, and various file types to a wide range of destinations.

C#GPL-3.0updated Nov 12, 2026
GitHub โ†—โ˜… 41Kโ‘‚ 4.1K
OCR & Documents#8

naptha/tesseract.js

โ–ฒ +18 โ˜… this week

Pure Javascript OCR for more than 100 Languages ๐Ÿ“–๐ŸŽ‰๐Ÿ–ฅ

JavaScriptApache-2.0updated May 17, 2026
GitHub โ†—โ˜… 38.8Kโ‘‚ 2.4K
OCR & Documents#9

ocrmypdf/OCRmyPDF๐Ÿ”ฅ active

โ–ฒ +61 โ˜… this week

OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched

PythonMPL-2.0updated Nov 9, 2026
GitHub โ†—โ˜… 35.3Kโ‘‚ 2.5K
OCR & Documents#10

opendataloader-project/opendataloader-pdf๐Ÿ”ฅ active

โ–ฒ +110 โ˜… this week

PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.

๐Ÿ” Search & Retrieval
JavaApache-2.0updated Nov 12, 2026
GitHub โ†—โ˜… 30.3Kโ‘‚ 2.9K
OCR & Documents#11

JaidedAI/EasyOCR

โ–ฒ +11 โ˜… this week

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

๐Ÿ” Search & Retrieval
PythonApache-2.0updated Dec 5, 2025
GitHub โ†—โ˜… 30.1Kโ‘‚ 3.6K
OCR & Documents#12

microsoft/unilm

โ–ฒ +2 โ˜… this week

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

๐Ÿง  Natural Language Processing
PythonMITupdated Sep 21, 2026
GitHub โ†—โ˜… 22.2Kโ‘‚ 2.7K
OCR & Documents#13

firecrawl/pdf-inspector

Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.

RustMITupdated Oct 22, 2026
GitHub โ†—โ˜… 20Kโ‘‚ 1.4K
OCR & Documents#14

lukas-blecher/LaTeX-OCR

โ–ฒ +11 โ˜… this week

pix2tex: Using a ViT to convert images of equations into LaTeX code.

PythonMITupdated Jan 18, 2025
GitHub โ†—โ˜… 16.6Kโ‘‚ 1.3K
OCR & Documents#15

Unstructured-IO/unstructured๐Ÿ”ฅ active

โ–ฒ +46 โ˜… this week

Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.

๐Ÿง  Natural Language Processing๐Ÿ” Search & Retrieval
HTMLApache-2.0updated Nov 6, 2026
GitHub โ†—โ˜… 15.7Kโ‘‚ 1.4K
OCR & Documents#16

yusufkaraaslan/Skill_Seekers

โ–ฒ +19 โ˜… this week

Convert documentation websites, GitHub repositories, and PDFs into Claude AI skills with automatic conflict detection

PythonMITupdated Sep 30, 2026
GitHub โ†—โ˜… 15.3Kโ‘‚ 1.6K
OCR & Documents#17

tisfeng/Easydict๐Ÿ”ฅ active

โ–ฒ +80 โ˜… this week

ไธ€ไธช็ฎ€ๆดไผ˜้›…็š„่ฏๅ…ธ็ฟป่ฏ‘ macOS Appใ€‚ๅผ€็ฎฑๅณ็”จ๏ผŒๆ”ฏๆŒ็ฆป็บฟ OCR ่ฏ†ๅˆซ๏ผŒๆ”ฏๆŒๆœ‰้“่ฏๅ…ธ๏ผŒ๐ŸŽ ่‹นๆžœ็ณป็ปŸ่ฏๅ…ธ๏ผŒ๐ŸŽ ่‹นๆžœ็ณป็ปŸ็ฟป่ฏ‘๏ผŒOpenAI๏ผŒGemini๏ผŒDeepL๏ผŒGoogle๏ผŒBing๏ผŒ่…พ่ฎฏ๏ผŒ็™พๅบฆ๏ผŒ้˜ฟ้‡Œ๏ผŒๅฐ็‰›๏ผŒๅฝฉไบ‘ๅ’Œ็ซๅฑฑ็ฟป่ฏ‘ใ€‚A concise and elegant Dictionary and Translator macOS App for looking up words and translating text.

SwiftGPL-3.0updated Nov 11, 2026
GitHub โ†—โ˜… 15.3Kโ‘‚ 812
OCR & Documents#18

sml2h3/ddddocr

โ–ฒ +19 โ˜… this week

ๅธฆๅธฆๅผŸๅผŸ ้€š็”จ้ชŒ่ฏ็ ่ฏ†ๅˆซOCR pypi็‰ˆ

PythonMITupdated Mar 10, 2026
GitHub โ†—โ˜… 14.9Kโ‘‚ 2.4K
OCR & Documents#19

HIllya51/LunaTranslator๐Ÿ”ฅ active

โ–ฒ +111 โ˜… this week

่ง†่ง‰ๅฐ่ฏด็ฟป่ฏ‘ๅ™จ / Visual Novel Translator

C++GPL-3.0updated Nov 12, 2026
GitHub โ†—โ˜… 14.1Kโ‘‚ 1.2K
OCR & Documents#20

run-llama/liteparse๐Ÿ”ฅ active

โ–ฒ +53 โ˜… this week

A fast, helpful, and open-source document parser

RustApache-2.0updated Nov 12, 2026
GitHub โ†—โ˜… 13Kโ‘‚ 899
OCR & Documents#21

datalab-to/chandra

โ–ฒ +24 โ˜… this week

OCR model that handles complex tables, forms, handwriting with full layout.

PythonApache-2.0updated Oct 22, 2026
GitHub โ†—โ˜… 12.7Kโ‘‚ 1.3K
OCR & Documents#22

DayBreak-u/chineseocr_lite

โ–ฒ +1 โ˜… this week

่ถ…่ฝป้‡็บงไธญๆ–‡ocr๏ผŒๆ”ฏๆŒ็ซ–ๆŽ’ๆ–‡ๅญ—่ฏ†ๅˆซ, ๆ”ฏๆŒncnnใ€mnnใ€tnnๆŽจ็† ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) ๆ€ปๆจกๅž‹ไป…4.7M

C++GPL-2.0updated May 18, 2026
GitHub โ†—โ˜… 12.4Kโ‘‚ 2.3K
OCR & Documents#23

dataelement/bisheng๐Ÿ”ฅ active

โ–ฒ +171 โ˜… this week

BISHENG is an open-source enterprise Agent platform for building, deploying, and managing AI applications. It combines AI agents, workflows, RAG and knowledge management, desktop agent integration, and vibecoding app hosting with unified model management, evaluation, observability, access control, and enterprise administration.

๐Ÿ” Search & Retrieval
PythonApache-2.0updated Nov 12, 2026
GitHub โ†—โ˜… 12.3Kโ‘‚ 2K
OCR & Documents#24

getomni-ai/zerox

OCR & Document Extraction using vision models

TypeScriptMITupdated May 20, 2025
GitHub โ†—โ˜… 12.3Kโ‘‚ 841
OCR & Documents#25

pymupdf/PyMuPDF๐Ÿ”ฅ active

โ–ฒ +49 โ˜… this week

PyMuPDF is a high performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents.

๐Ÿ’Ž Text Mining
PythonAGPL-3.0updated Nov 12, 2026
GitHub โ†—โ˜… 11.1Kโ‘‚ 837
OCR & Documents#26

CVHub520/X-AnyLabeling๐Ÿ”ฅ active

โ–ฒ +56 โ˜… this week

X-AnyLabeling: A lightweight, efficient, and unified cross-platform desktop application for annotating text, image, video, point cloud and multimodal data, combining versatile built-in tools with state-of-the-art AI models and flexible multi-format export.

PythonGPL-3.0updated Nov 8, 2026
GitHub โ†—โ˜… 11Kโ‘‚ 1.2K
OCR & Documents#27

zyddnys/manga-image-translator๐Ÿ”ฅ active

โ–ฒ +18 โ˜… this week

Translate manga/image ไธ€้”ฎ็ฟป่ฏ‘ๅ„็ฑปๅ›พ็‰‡ๅ†…ๆ–‡ๅญ— https://cotrans.touhou.ai/ https://greasyfork.org/en/scripts/437569-cotrans-manga-image-translator-regular-edition

๐ŸŒ Machine Translation
PythonGPL-3.0updated Nov 11, 2026
GitHub โ†—โ˜… 10.6Kโ‘‚ 1.1K
OCR & Documents#28

ripperhe/Bob

Bob ๆ˜ฏไธ€ๆฌพ macOS ๅนณๅฐ็š„็ฟป่ฏ‘ๅ’Œ OCR ่ฝฏไปถใ€‚

Otherno licenseupdated Dec 30, 2025
GitHub โ†—โ˜… 9.8Kโ‘‚ 520
OCR & Documents#29

YaoFANGUK/video-subtitle-extractor

โ–ฒ +24 โ˜… this week

่ง†้ข‘็กฌๅญ—ๅน•ๆๅ–๏ผŒ็”Ÿๆˆsrtๆ–‡ไปถใ€‚ๆ— ้œ€็”ณ่ฏท็ฌฌไธ‰ๆ–นAPI๏ผŒๆœฌๅœฐๅฎž็Žฐๆ–‡ๆœฌ่ฏ†ๅˆซใ€‚ๅŸบไบŽๆทฑๅบฆๅญฆไน ็š„่ง†้ข‘ๅญ—ๅน•ๆๅ–ๆก†ๆžถ๏ผŒๅŒ…ๅซๅญ—ๅน•ๅŒบๅŸŸๆฃ€ๆต‹ใ€ๅญ—ๅน•ๅ†…ๅฎนๆๅ–ใ€‚A GUI tool for extracting hard-coded subtitle (hardsub) from videos and generating srt files.

PythonApache-2.0updated Apr 9, 2026
GitHub โ†—โ˜… 9.7Kโ‘‚ 986
OCR & Documents#30

xberg-io/xberg๐Ÿ”ฅ active

The fast, precise document-intelligence engine for every language: text, tables and metadata from 100+ formats, with OCR, transcription and code intelligence.

๐Ÿ” Search & Retrieval
RustMITupdated Nov 12, 2026
GitHub โ†—โ˜… 9.6Kโ‘‚ 615

๐Ÿ“„ New research on OCR & Documents

๐Ÿ“„ arXiv:2611.13752 ยท cs.CL2d ago

Word-Level Alignment of OCR and Ground Truth under Zoning and Reading-Order Errors

Fonseca Cacho, Jorge Ramรณn

๐Ÿ’ฌ 19 pages. Submitted to IJDAR (ICDAR 2027 Journal Track). Code: https://github.com/SniperJF/zone-robust-ocr-align

OCR post-processing needs OCR text aligned word by word with its ground truth. Data sets rarely provide that mapping, and a monotone aligner can lose its place when zoning errors permute the reading order. We revisit this problem, the Zoning Problem of our 2019 paper, with a protocol that tests correctness under such permutations. We apply OCR-side reading-order permutations with construction-time word-level gold and score correct-match F1, cascade length and recovery. We compare the 2019 location-aware aligner, a re-anchoring extension of it, a RETAS-style baseline (RETAS-lite), a multi-region aligner that combines unique-word and unique n-gram anchors with chunk-to-window assignment, genalog's RETAS and the original Java RETAS. Experiments use synthetic character noise with block moves, paragraph swaps and column interleaving, historical newspaper reading-order permutations, geometry-derived gold for Tesseract OCR, and a small word-box set. Synthetic tests show where local tracking fails after large displacements and where unique-anchor methods collapse when unique anchors are scarce. On our synthetic boilerplate page the 2019 method had the highest F1 among those tested. Historical newspaper and geometry-derived evaluations carry the limitations of approximate gold and the article-order convention. The first multi-region version has a quadratic scaling cost. Local tracking remains fragile under large reading-order changes; the tested anchor-based methods handle many of those cases but fail when unique anchors are scarce. Approximate gold, the article-order convention, missing residual annotation, and the lack of modern multi-column word-level ground truth remain limitations.โ–ผ more
๐Ÿ“„ OCR & Documents
๐Ÿ“„ arXiv:2611.13609 ยท cs.CL2d ago

Post-OCR Correction of Mixed-Script Uzbek Text: Byte-Level ByT5 with an Empirical Homoglyph Noise Matrix

Mannon Ochilov, Oybek Narzullayev, Bahodir Zaripov

๐Ÿ’ฌ 7 pages, 2 figures, 7 tables. Accepted at the 10th International Conference on Future Networks and Distributed Systems (ICFNDS 2026), ACM ICPS. Code: https://github.com/OybekNarzullaev/byt5-uzbek-postocr

In Uzbek official and technical documents, a large share of OCR errors is caused not by poor image quality but by characters that are visually identical across the two alphabets in use. Because the script transition is still under way, Cyrillic and Latin appear on the same page and often within a single sentence, and in our measurements the density of homoglyph errors on such pages is close to five times higher than in single-script text. We study this problem in a setting where the corpus of engineering-geological and ICT infrastructure reports may not leave the organisation, so every stage was executed on local hardware. The proposed system combines a byte-level ByT5 model with a noise generator whose sampling weights are estimated from 359 manually classified error events, together with a Levenshtein-bounded conservative decoding rule. On a Gold set of 5,204 line pairs it lowers the character error rate from 0.0578 to 0.0369, restores 91.3% of the homoglyphs and introduces only 84 new errors.โ–ผ more
๐Ÿ“„ OCR & Documents
๐Ÿ“„ arXiv:2611.12691 ยท cs.CL3d ago

A Lattice-Based HMM Approach to Aruz Scansion of Ottoman Poetry

Mehmet Emre Tiryaki

๐Ÿ’ฌ 14 pages, 5 figures, 8 tables. Code: https://github.com/MehmetETiryaki/aruz-paper. Web tool: https://mehmetemretiryaki.com/aruz

Ottoman Divan poetry is composed in aruz, a quantitative meter based on the weights of syllables. The meter is sensitive to specific characters recorded in the original writing in the Arabic script. This work is concerned with automatic meter finding and scansion alignment for Ottoman Divan poetry, especially on modern Turkish spelling without the special diacritics that scholarly transliterations of Ottoman preserve. We define two tasks on this input, first identifying the meter used in the poem, and second aligning the meter's weights with the syllabification of the poem, identifying the flaws and freedoms the poet has taken along the way. We build a corpus of 52,000 lines split by poet, with scansion annotations for 500 test lines, and a robustness benchmark generated at the deviation rates measured on real web and OCR copies. Our model is a hidden Markov model over the positions of each meter, decoded over a lattice of every candidate path, and trained by EM from the printed meter, without scansion labels. On a held-out set it scans 77.6% of the test lines fully correctly, against 26.8% for the available rule-based scanner, 55.0% for a neural tagger trained with the same supervision and 63.6% for Claude Opus 5.5 few-shot. The code is open-sourced and the tool is released on the web.โ–ผ more
๐Ÿ“„ OCR & Documents
๐Ÿ“„ arXiv:2611.12240 ยท cs.CL3d ago

Prune, Don't Merge: Expert Compression of Mixture-of-Experts LLMs on Multilingual Production Tasks

Tomas Rasymas, Povilas Kvedaras

๐Ÿ’ฌ 38 pages, 8 figures, 15 tables

Mixture-of-Experts language models are cheap to run per token but expensive to hold in GPU memory, which drives the cost of serving them in production. One-shot expert compression shrinks them after a single calibration pass. Its two current state-of-the-art methods are REAP for expert pruning, which deletes the least salient experts, and REAM for expert merging, which merges them into the most salient ones. Both have been evaluated only on academic benchmarks. We compare the two on five multilingual production tasks of a web-hosting company, from classification to the generation of marketing HTML email templates, with two 30B-parameter models (Qwen3-30B-A3B-Instruct-2507 and the hybrid Mamba-attention Nemotron-3.5-Lightning) at 25% and 50% of experts removed and the same calibration data. Pruning wins: on the same rows, REAP is significantly better in 16 of 20 comparisons and REAM in 2. At 25%, REAP keeps 93.6% of Qwen3-30B's generative quality and REAM 62.5%, although on benchmarks the two are within a point for the same model. The damage concentrates in the output contract rather than the content: 89.8-95.2% of each significant loss on email templates comes from hard-gate failures such as invalid JSON, broken mobile layouts and runaway generation, and most failed non-English domain descriptions copy the prompt's English opening phrase. Cross-perplexity and logit divergence, two diagnostics from the REAP paper, also track production quality, as does the rate of repetition loops; n-gram diversity does not. We close with practical guidance for compressing MoE models for production.โ–ผ more
๐ŸŒ Machine Translation๐Ÿ“„ OCR & Documents๐Ÿค– Language Models