# "1M parameters, 17x faster than Tesseract on CPU": an established OCR team built it the opposite way, 24 days, 1,600 stars · GitHub open-source project

> Source: https://stringers.io/en/github/gh_datalab-to__indra.html (Stringers / 什么值得做, generated 2026-11-08)
> Stringers answers one question every day: what small product can one or two people build in a few months and actually make money from. Numbers come from 15 databases we collect daily, each with source and date.
> For agents: these verdicts and the underlying databases (verified revenue, App Store / Google Play charts, ad libraries, GitHub, Product Hunt…) are queryable live over MCP — tools today / hot / products / directions / direction / data. Setup: https://github.com/komako-workshop/stringers (the user logs in at stringers.io → "配置 agent" to get a personal URL). Site index: https://stringers.io/llms.txt

> People building RAG ingestion mostly have only CPUs. Tesseract is inaccurate, PaddleOCR is slow, and new large-model OCR needs a GPU. Indra fills that gap. The "a thousand times fewer parameters, over ten times faster than Tesseract" contrast, plus the founder's own X account posting three times a week, lines up with all three star spikes.

[GitHub open-source project](https://stringers.io/en/github/index.md) · written 11-6

datalab-to/indra · datalab-to

# "1M parameters, 17x faster than Tesseract on CPU": an established OCR team built it the opposite way, 24 days, 1,600 stars

**What it is** An open-source OCR package that runs on CPU alone. It turns PDFs and images into Markdown with headings, tables and reading order, or JSON with coordinates. The main model is only about 1M parameters.

**Why it took off** Most people building RAG ingestion only have CPUs. Tesseract is inaccurate, PaddleOCR is slow, and new large-model OCR needs a GPU. This fills that gap. The contrast number, "a thousand times fewer parameters, over ten times faster than Tesseract", plus the founder's X account posting three times a week, lines up with all three star spikes.

**C** **How you could approach it** Don't build another CPU OCR or a port of this one. Go after what it doesn't do well: Chinese scanned books, curved pages and skewed tables. Or apply the "go small in reverse" approach to a different task that only large GPU models handle today. Include your own benchmark set and a local comparison viewer, and open the launch post with the contrast number.

Grade C: the "go small in reverse, lead with the contrast number" playbook is worth learning. But its stars mostly come from a team with 79k stars and the founder's account. Even with those resources, its sister project Chandra only got about 1/8, so copying it or building a port won't earn much. What people in niche circles are saying

Data (updated daily) · Repo created 2026-10-13 · 1,663 stars · 7 days +126

GitHub · stars

Data 2026-10-13 → 2026-11-08 (27 days, 26 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

2026 Oct 13 (US time), Datalab founder Vik Paruchuri posted on X: "Introducing Indra, a 1M parameter OCR model: 49 pages/s on CPU, 11x faster than PaddleOCR and 17x Tesseract." Under it, @vikhyatk, the author of Moondream, replied with one word: "woah!" The post picked up about 2,900 likes and 150k views. The next day, Chinese tech blogger 程序员鱼皮 passed it along: "Lots of people still think OCR means running a big model on a GPU. Turns out the newly open-sourced Indra... is a good fit for RAG knowledge bases." By Nov 6, the GitHub repo datalab-to/indra had 1,643 stars and 168 forks, and star growth had already slowed. This piece looks at where its stars came from, and what you can and can't learn from it if you want to build an open-source project that gets stars too.

### What it is

A Python package, installed in one line with `pip install indra-ocr`. It turns PDFs and images into Markdown, plain text, or JSON with per-line coordinates. Headings, lists, tables and footnotes are separated, and reading order is set. Digital PDFs read the text layer first and only OCR what's missing. It depends only on ONNX Runtime: no PyTorch, no GPU. The default model download is about 76MB. "1M parameters" refers to the main recognition model (about 1.25M); all models together total 7.3M (per the README). It also ships a local web viewer for side-by-side comparison with Tesseract. The code is Apache 2.0, and the weights use a modified OpenRAIL-M license: free if revenue or funding is under $20M, and you can't use it to run a service that competes with their API.

Datalab is not new to this. Marker (40.8k stars), Surya (21.6k stars) and Chandra (12.7k stars) all come from them. The org's 7 repos total about 79k stars.

### Where the stars came from: the founder posted three times in a week

| Date | What happened | Stars (GitHub daily snapshot) |
|---|---|---|
| 10-13 | Repo created. Founder launch post, about 2,900 likes | — |
| 10-14 | Chinese tech blogger 程序员鱼皮 relay (573 likes, 45k impressions), followed by Japanese bloggers | — |
| 10-17 | Founder's second post, "I still can't believe how good this model is", about 2,900 likes | 393 |
| 10-18 | Single day +152 | 545 |
| 10-19 to 10-20 | Version 1.1 release: 80 multiple languages, handwriting, any angle. Founder's third post got about 3,000 likes and 230k impressions | 644 |
| 10-21 | Single-day peak +165 | 809 |
| 10-28 | Another high +154, source not found (likely a YouTube review or a Japanese community) | 1,302 |
| 11-06 | Last 7 days +217; the previous 7 days +487, cooling off | 1,643 |

The three jumps line up almost one-to-one with the founder's three posts. A Hacker News blog post got only 39 points and 0 comments and didn't help. No mainstream tech press covered it. What's left is a long tail: YouTube reviews with titles like "RIP Google Tesseract?" and "25 Pages/Second on My MacBook", Reddit and LinkedIn posts, and a batch of SEO articles.

Another channel was built into the design: the model is small and ONNX-only, so others can port it easily. Within two weeks, ports appeared for WASM in the browser, Rust, C#, Go and TypeScript, along with a Docling plugin, a FastAPI service, and a Japanese open-source bookkeeping project called Ryoshu that integrated it. Each derivative has only single or low double-digit stars, but they carried the Indra name into other language communities.

### Why it took off

1. **It hit a position nobody was seriously building for.** People doing RAG ingestion and search indexing mostly have only CPUs. Tesseract is inaccurate and only outputs text. PaddleOCR's full layout pipeline takes around 40 seconds per page on CPU. The new OCR models of the past couple of years (DeepSeek-OCR, GLM-OCR, dots.ocr, MiniCPM-OCR and similar) are all 1B+ parameters and need a GPU. "Small model, CPU, still outputs layout and Markdown" is a niche that this is the first well-known team to build seriously.

2. **The headline number is counterintuitive.** 1M parameters versus 1B+ is a thousand-fold gap. All three posts lead with it, and Yu Pi's relay also starts there: "you need a GPU for OCR" is wrong.

3. **The founder's reputation vouched for it up front.** When a team that built Marker and Chandra releases a new OCR model, people assume it's worth a try. That skipped the cold-start problem.

4. **A major version came out within 6 days of launch.** Version 1.1 added languages, handwriting and rotated scans. The biggest single-day star jump came right after the 1.1 release.

### What the comments and hands-on tests say

The loudest pushback came from the Chinese-speaking community. @manateelazycat tested it on 50 pages of Chinese scanned books and found that by default it's only 1.24x faster than PaddleOCR. Even after his own deep optimization it was only 2.97x faster. He said "the 'faster by 11x' figure is data from 32 threads stacked together", and it failed on curved, cross-page Chinese scans. Someone replied under it: "32x from 11 threads stacked, this benchmark marketing smells like crypto project founders reporting TPS." Under Yu Pi's post, someone posted a screenshot saying "the recognition is worse than Qwen's."

The independent tests from the English-speaking world say much the same. Daniel van Strien of Hugging Face tested it on 2,165 pages of historical books and concluded it "runs on CPU and ties Tesseract". A Medium hands-on test found that table recognition dropped to zero when the page was skewed by 7 degrees.

Another detail: all the comparison numbers come from OmniParseBench, a benchmark Datalab released at the same time, and the official figures have changed. The launch post said "17x Tesseract, 11x PaddleOCR, 49 pages per second". The current README says "15x" and "10x" and about 44 pages per second (all on 32 cores). Writing the test and judging it yourself lets you define the track, but it also invites independent testing.

None of this criticism has made it lose stars, but it explains why it has stalled at about 1,600. It's a tool for ingestion that's "good enough and fast", not the most accurate. Chandra, from the same team, was open-sourced a year earlier and has 12.7k stars and 1.39M downloads on Hugging Face. Indra is at about 1/8 of that, with 7,810 downloads on HF. The README says it outright: for the highest accuracy, use Chandra on our hosted platform.

### Who it's competing with

| Project | Scale (GitHub) | Compared with Indra |
|---|---|---|
| Tesseract | 77.4K stars | The long-standing default option. Outputs text lines only, no layout |
| PaddleOCR | 92.4K stars | Strongest on Chinese, with a full document pipeline, but much slower on CPU |
| RapidOCR | 8,500 stars | Also a lightweight ONNX + CPU approach, but only outputs text lines |
| EasyOCR / docTR | 30.1k / 6,400 stars | PyTorch-based, does detection and recognition only |
| ocrs(Rust) | 1,900 stars | Small model, CPU, text only, few languages |
| DeepSeek-OCR, dots.ocr, GLM-OCR and others | Thousands to 30k stars | 1B+ parameters, needs a GPU, more accurate |

No independent competitor with the same positioning currently exists. Following behind it are only ports and wrappers, all with very few stars.

### If it were you

**The short verdict:** The approach is worth learning, but don't copy the project. "When everyone is scaling up to large models, go the other way and build a tiny version that runs on CPU, leading with a number that is a thousand times smaller": this move can be applied to other niches. But more than half of Indra's 1,600 stars came from the founder's account, which has 79k followers, and even with that account, it only reached about 1/8 of Chandra's stars. Building another CPU OCR, or a port of this one, won't earn many stars.

**Who it's for:** Devs who know model compression, distillation, and ONNX deployment, and can get a narrow use case to "good enough"; people with domain data who can build their own eval sets.

**Who it's not for:** People without an existing audience who expect one post to take off (Indra's HN post only got 39 points); people trying to go head-to-head on accuracy with general-purpose OCR like PaddleOCR and the wave of big models; people who just want to port it and ride the hype, since existing ports only have single-digit stars.

**How to build it:** Two paths. One: fill the gaps Indra leaves. Chinese scanned books, curved two-page spreads, and skewed tables are all called out in Chinese hands-on tests and independent evals. Build a Chinese-focused CPU document parser and beat both Indra and PaddleOCR on the same set of pages. Two: take the "make it small" approach to other tasks, like table structure recognition, layout detection, speech transcription, or document classification. Find something only GPU-scale models can do today and ship a few-MB version that runs on CPU. Either way, do three things: make it install in one line with few dependencies so people will port it; ship a local comparison viewer so people can verify it themselves; and state the speed numbers with the core count they were measured on, so nobody can catch you out.

**How to get users:** Open the launch post with the contrast numbers (how many times smaller, how many times faster), plus a scatter plot of speed vs. accuracy. Open-source your eval set so comparisons have something to check against. For a Chinese project, send it to developer creators like 程序员鱼皮 who benchmark things themselves; for English, send it to X accounts and YouTube reviewers covering OCR and RAG. Within a week of launch, ship a bigger release that patches the weak spots to catch the second wave.

**When to move:** On-device and CPU inference are still hot topics, and new large-model OCR tools keep arriving every month, so this contrast is best told now. Estimated: build before the first half of 2027, while the window is still open (inference).

**Signs you've misread it:** If the hands-on test posts outnumber the reposts and they're all saying "not as fast as claimed," your numbers were set too high. If PaddleOCR or a major company ships an official lightweight CPU layout model, the "small and complete" spot is taken. If your project is getting three posts a week and still isn't reaching a few hundred stars, the gap in this space is distribution, not product.

[GitHub repo ↗](https://github.com/datalab-to/indra)

**Research notes: competitor table, acquisition evidence, full research report**

Competitors

| Name | Where | Launched | Scale | How it differs from this one |
|---|---|---|---|---|
| Tesseract | Open source | 2006 open source / GitHub 2014 | 77.4k stars | Outputs text lines only, no layout; Indra self-reported 15x faster, text score 41.3 vs 19.3 |
| PaddleOCR(PP-OCR / PP-StructureV3) | Open source | 2020 | 92.4k stars | Best Chinese quality with a full document pipeline, but extremely slow on CPU (about 41 seconds per page); Indra is about 0.23 seconds per page, and falls short of it on curved Chinese scans |
| RapidOCR | Open source | 2021 | 8.5k stars | Also goes the ONNX + CPU lightweight route, but outputs text lines only, no layout or Markdown |
| EasyOCR / docTR | Open source | 2020 / 2021 | 30.1k / 6.4k stars | PyTorch-based and slower; detection and recognition only |
| ocrs | Open source | 2024-01 | 1.9k stars | Small Rust model on CPU, but text output only and few languages |
| DeepSeek-OCR / OCR-2, GLM-OCR, dots.ocr, Xiaomi-OCR-0, MiniCPM-OCR, baidu Unlimited-OCR | Open source | 2025-07 to 2026-10 | About 0.8k–30.7k stars | VLMs with 1B+ parameters, need a GPU, high accuracy; Indra trades some accuracy for CPU throughput |
| Datalab Chandra / Marker / hosted API | Open source | 2023–2025 | Chandra 12.7k stars, Marker 40.8k stars | Same company's high-accuracy line; Indra is its low-end entry point and funnel |
| Google Document AI / AWS Textract / Azure Read / Apple Vision | Big-company features | Years | Big | Cloud billing per page or platform lock-in; Indra runs locally for free |

Where users come from

- **Verified** **founder post on X** : Vik Paruchuri's three posts got 2,899 / 2,938 / 3,003 likes respectively 10-14 / 10-17 / 10-20, and the star jumps line up with them (x live 11-06)

- **Verified** **Chinese and Japanese tech creators relaying and reviewing it on X** : @yupi996 573 likes / 45k views; @Gorden_Sun 89 likes; @manateelazycat's skeptical hands-on test 91 likes; Japan's @taziku_co and others

- **Verified** **YouTube / Reddit / LinkedIn / SEO articles** : YouTube reviews like "RIP Google Tesseract?"; r/LovingOpenSourceAI; articles on explainx, activepieces, etc. (Jina search)

- **Verified** **HN** : the blog post got 39 points and 0 comments (id 50077166); negligible impact

- Inferred **Existing users of the company's projects (Marker / Surya / Chandra, Discord, HF org)** : sibling repos have 75k+ stars; the README and HF pages link to each other; no quantified contribution to star count

- **Verified** **Self-built benchmark OmniParseBench and independent evals** : all comparison numbers come from their own benchmark; drew in Daniel van Strien's historical book eval and a Medium hands-on test

- **Verified** **Community ports** : within two weeks, 20+ derivative libraries appeared, including WASM, Rust, C#, TS, Go, and a Docling plugin (GitHub search)

## Why datalab-to/indra (Indra OCR) took off

> Channel: GitHub | Research date 2026-11-06 | Data card: /home/ljr/fanout/why/cards/gh_datalab-to__indra.json

### One line

Indra OCR is an OCR package open-sourced by Datalab (the company behind Marker / Surya / Chandra) 2026-10-13. Its main recognition model has 1M parameters, runs on CPU only, uses only ONNX Runtime, outputs Markdown directly from PDFs and images, and can also output JSON with coordinates. It took off for two reasons. First, founder Vik Paruchuri's own X account posted three times in one week, each post getting nearly 3,000 likes. Second, the line "15x faster than Tesseract on CPU and more accurate" hits the pain point of everyone doing RAG ingestion. Its differentiation is in a spot nobody has seriously worked on for years: "small, fast, CPU, with layout", because everyone has been chasing VLM OCR models of 1B+ parameters. That differentiation is really why it got seen. Still, its star count (24k in 1.6 days) is far below its sibling Chandra (12.7k), suggesting the niche is a real need but not a flashy one.

### Data

- GitHub: 2026-10-13 repo created. Our GitHub repo 10-17 recorded 393 stars, 11-06 recorded 1,643 stars and 168 forks (forks/stars about 10%). The single biggest daily gain was on 10-21, +165, right after the 1.1 release; two other peaks were 10-18 (+152) and 10-28 (+154). Last 7 days +217, previous 7 days +487, now cooling off (source: github repo_profile/repo_day).

- Releases: v0.3.1 (10-13), v1.1.0/1.1.1/1.1.2 (10-19 to 10-20); 11-04 is still merging external PRs (GitHub API).

- Hugging Face model datalab-to/indra-ocr: 513 likes, 7,810 downloads (HF API, 11-06). The HF Space has only 14 likes.

- Sibling projects for comparison: Chandra (2025-10 open source) 12,694 stars, chandra-ocr-2 has 1.39M downloads on HF; Marker 40.8k; Surya 21.6k (GitHub API / github repo).

### Competitors (local / CPU OCR that turns scanned documents into text)

| Name | Where | Launched | Scale | How Indra differs |
|---|---|---|---|---|
| Tesseract | Open source | Open source since 2006, GitHub 2014 | 77.4k stars | The long-standing default option; outputs text lines only, no layout; Indra self-reported 15x faster, text score 19.3 vs 41.3 |
| PaddleOCR(PP-OCR / PP-StructureV3) | Open source (Baidu) | 2020 | 92.4k stars | Strongest for Chinese, with a full document pipeline, but very slow on CPU (PP-StructureV3 takes about 41 seconds per page); Indra takes about 0.23 seconds per page and falls short on curved Chinese scans (hands-on test by @manateelazycat) |
| RapidOCR | Open source | 2021 | 8.5k stars | Runs PP-OCR converted to ONNX, also a lightweight CPU route, but outputs text lines only; Indra adds layout, reading order, and tables |
| EasyOCR / docTR / OnnxTR | Open source | 2020–2022 | 30.1k / 6.4k / 198 stars | PyTorch-based or ONNX wrapper; text detection and recognition only, no Markdown or layout output |
| ocrs(Rust) | Open source | 2024-01 | 1.9k stars | Also a small model + CPU, but text only, few languages |
| chineseocr_lite | Open source | 2020 | 12.4k stars | 4.7M ultra-lightweight Chinese OCR, no layout, long since unmaintained |
| DeepSeek-OCR / OCR-2, GLM-OCR, dots.ocr, HunyuanOCR, Xiaomi-OCR-0, MiniCPM-OCR, baidu Unlimited-OCR | Open-source VLMs | 2025-07 to 2026-10 | 24.1k / 3.5k / 7.6k / 9.2k / 956 / 838 / 30.7k stars | They all need 1B+ parameters and a GPU for high accuracy; Indra uses only 1/1000 parameters, trading for CPU throughput, and claims it's just 4 points behind DeepSeek OCR 2 on OmniParseBench |
| Datalab's own Chandra / Marker / hosted API | Open source + SaaS | 2023–2025 | 12.7k / 40.8k stars | The same company's high-accuracy line; Indra is its low-end entry point, and the README funnels users straight to the API (5,000 pages/month free, with utm_source=gh-indra) |
| Big-company features: Apple Vision, Google Document AI, Azure Read, AWS Textract | Big-company features / cloud | Years | Very large | Cloud billing per page or platform lock-in; Indra runs locally and is free |

Bottom line: the veterans of the "small CPU model OCR" space (Tesseract, the PP-OCR family, ocrs) output text only; those doing "layout plus Markdown" are large GPU models. Indra is the first from a well-known team to seriously occupy both spots at once.

### Growth tactics (where users come from)

1. **Founder's X account (verified, main channel)** : Vik Paruchuri's three posts: the launch post 10-14 got 2,899 likes / 150k views; "I still can't believe how good this model is" 10-17 got 2,938 likes; 10-20 1.1 release post got 3,003 likes / 230k views (checked live on X, 11-06). The three star jumps (10-14 to 10-17, 10-18, 10-21) line up one-to-one with these three posts. @vikhyatk (Moondream's creator) also replied with "woah!"

2. **Relayed by Chinese and Japanese tech creators (verified)** : @yupi996 (程序员鱼皮) 10-14 posted 573 likes / 45k views, testing it himself on an M2 Max; @Gorden_Sun 89 likes; @manateelazycat posted a skeptical hands-on test with 91 likes (on Chinese content it's only 1.24–2.97x faster, and it fails on curved scans); Japan's @taziku_co and @shion_takk also posted. There are also Reddit r/LovingOpenSourceAI threads, LinkedIn posts, several YouTube reviews ("RIP Google Tesseract?" and "25 Pages/Second on My MacBook"), and content-farm-style SEO articles on explainx, activepieces, and hermes-ai (Jina search).

3. **HN didn't take off (verified)** : the datalab.to/blog/indra-ocr post got only 39 points and 0 comments (HN id 50077166).

4. **The company's existing users (inferred, high confidence)** : Marker, Surya, and Chandra together have 75k+ stars; the company's Discord and HF org page both pointed people to it; the README and HF link to each other.

5. **Self-built benchmark (verified)** : they released their own benchmark, OmniParseBench (datalab-to/omni_parse_bench), at the same time, and every comparison number comes from it. This is the playbook of defining the category and being your own judge, and it drew in independent evals: Daniel van Strien (HF) tested it on 2,165 pages of historical books, concluding it "runs on CPU and ties Tesseract"; on Medium, gnana70's hands-on test found tables dropped to zero at a 7° skew.

6. **Ecosystem ports (confirmed)** : within two weeks, WASM browser builds (RyanCasas, pg0, fusor-rs), a Rust port (Ziqi-Yang), C# / .NET, a TypeScript SDK, Go, a Docling plugin, a FastAPI server, and Ryoshu, a Japanese open-source accounting app (@studio_yebisu, 158 likes). The model is only 76MB and runs on ONNX, so porting is cheap, and those ports carried it into other communities.

No evidence of paid acquisition found.

### Why it took off

- **Hits a real pain** : people building RAG ingestion and search indexes mostly only have CPUs. Tesseract is inaccurate, PaddleOCR is heavy and slow, and VLM OCR needs a GPU. "No GPU, 15x faster, with Markdown and layout output" fits that need exactly (the blog's "Why a small model?" section; a Jike post's words: "perfect for a RAG knowledge base").

- **Counterintuitive number** : 1M parameters versus 1B+ is a hook on its own, and all three posts open with it.

- **Built-in distribution** : the founder's personal posts get about 3,000 likes each; the team already had goodwill from Marker and Chandra, so people assumed "this team's OCR is worth a try."

- **Low barrier, run it now** : `pip install indra-ocr` plus a local viewer, and you can compare directly against Tesseract. No PyTorch, a 76MB model, so benchmarks and ports came fast.

- **Fast iteration caught the second wave** : a major version shipped 6 days after launch, adding 1.1 multilingual support, handwriting, and arbitrary rotation. The second star peak (10-21) came from that release (80).

### Differentiation

It meets both "small CPU model" and "full layout output with reading order and Markdown tables," plus a hybrid mode that reads the text layer of born-digital PDFs and only OCRs what's missing. That differentiation is why it got noticed: "1M parameters, CPU, 15x Tesseract" in the title is the differentiation itself. But accuracy has clear weak spots on Chinese, handwriting, and skewed tables (independent tests and Chinese-language trials both point this out), and people themselves describe it as a "good enough" triage and ingestion tool, not SOTA. So stars cooled off after 3 weeks, landing around 1/8 of Chandra's count.

### Visibility

It spread within small circles. On X's AI and OCR crowd it got roughly 9,000 likes of exposure, Chinese and Japanese tech bloggers reposted it, and there's a YouTube review. Hacker News didn't pick it up, and no mainstream tech outlet covered it. It's open source, so there's no public revenue to report.

### Copycats

No independent competitor with the same positioning exists yet. There's a batch of ports and wrappers built on it (WASM, Rust, C#, TS, Go, Docling plugin), each with single- to low-double-digit stars.

### What others can borrow

1. While everyone else scales models up, go the other way: build something very small that runs on CPU, and lead with a contrast number like "1000x fewer parameters, N x faster."

2. Publish your own benchmark and use it to define the category.

3. Use the open-source version as the funnel: code under Apache, weights under a modified OpenRAIL-M (free if revenue or funding is under $20M, no competing API), with a hosted API link carrying utm tags in the README.

4. Ship another major version within a week of launch to catch the second wave of attention.

5. Keep the deliverable small with few dependencies (ONNX only), and let the community port it to every language and browser for you.

### Still unclear

- Who brought the +154 stars on 10-28 (maybe YouTube or the Japanese community)? Not found.

- PyPI downloads: pypistats rate-limited, no data.

- How much it converted to Datalab's paid API: no public data.

- The 15x and 11x figures are the vendor's own tests on 32 cores; independent tests (Chinese text, historical books) show much smaller gains.

### Sources

- [github.com/datalab-to/indra](https://github.com/datalab-to/indra) (README, releases, commits)

- [www.datalab.to/blog/indra-ocr](https://www.datalab.to/blog/indra-ocr) ,[www.datalab.to/blog/indra-ocr-1-1](https://www.datalab.to/blog/indra-ocr-1-1)

- [x.com/VikParuchuri/status/2110058905875460393](https://x.com/VikParuchuri/status/2110058905875460393) ,/2111150820372931006 ,/2112216762767085883

- [x.com/yupi996/status/2110334301367845184](https://x.com/yupi996/status/2110334301367845184) ,[x.com/manateelazycat/status/2110539989218107644](https://x.com/manateelazycat/status/2110539989218107644) ,[x.com/vanstriendaniel/status/2113944664269099432](https://x.com/vanstriendaniel/status/2113944664269099432) ,[x.com/studio_yebisu/status/2115396604694085961](https://x.com/studio_yebisu/status/2115396604694085961)

- [news.ycombinator.com/item?id=50077166](https://news.ycombinator.com/item?id=50077166)

- [huggingface.co/datalab-to/indra-ocr](https://huggingface.co/datalab-to/indra-ocr) (HF API: 513 likes / 7,810 downloads)

- [gnana70.medium.com/indra-ocr-takes-on-tesseract-and-paddleoc…](https://gnana70.medium.com/indra-ocr-takes-on-tesseract-and-paddleocr-what-our-tests-found-90d797977988)

- [www.reddit.com/r/LovingOpenSourceAI/comments/1xfs5yw/](https://www.reddit.com/r/LovingOpenSourceAI/comments/1xfs5yw/)

- Our GitHub repo data (repo_profile, repo_day, as of 2026-11-06); the port list from a GitHub search for "indra-ocr"

This article was written by AI from public data and the data we collect daily. Numbers are sourced, and inferences are labeled.