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GLM-OCR
Python2026–2026
zai-org / GLM-OCR

GLM-OCR

GLM-OCR: Accurate × Fast × Comprehensive

AI & Machine Learning
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4Distinct people
5Mentions
0Explicit recommenders
7.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

GLM-OCR: Accurate × Fast × Comprehensive

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week5 dated mentions

Observed mentions: 2026-02-07 14:15:36 UTC to 2026-08-24 15:23:40 UTC. Weeks start Monday, UTC.

012026-02-02 00:00:00 UTC to 2026-02-09 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsFeb 22026-02-09 00:00:00 UTC to 2026-02-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-16 00:00:00 UTC to 2026-02-23 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-02-23 00:00:00 UTC to 2026-03-02 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-02 00:00:00 UTC to 2026-03-09 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-09 00:00:00 UTC to 2026-03-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-16 00:00:00 UTC to 2026-03-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-23 00:00:00 UTC to 2026-03-30 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-30 00:00:00 UTC to 2026-04-06 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-04-06 00:00:00 UTC to 2026-04-13 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-04-13 00:00:00 UTC to 2026-04-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-20 00:00:00 UTC to 2026-04-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-27 00:00:00 UTC to 2026-05-04 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-04 00:00:00 UTC to 2026-05-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-11 00:00:00 UTC to 2026-05-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMay 112026-05-18 00:00:00 UTC to 2026-05-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-25 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-01 00:00:00 UTC to 2026-06-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-08 00:00:00 UTC to 2026-06-15 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-15 00:00:00 UTC to 2026-06-22 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-22 00:00:00 UTC to 2026-06-29 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-29 00:00:00 UTC to 2026-07-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-06 00:00:00 UTC to 2026-07-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-13 00:00:00 UTC to 2026-07-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-20 00:00:00 UTC to 2026-07-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-27 00:00:00 UTC to 2026-08-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-08-03 00:00:00 UTC to 2026-08-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-08-10 00:00:00 UTC to 2026-08-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-08-17 00:00:00 UTC to 2026-08-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-08-24 00:00:00 UTC to 2026-08-31 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 24

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Feb 22026-02-02 00:00:00 UTC2026-02-09 00:00:00 UTC110
Feb 92026-02-09 00:00:00 UTC2026-02-16 00:00:00 UTC000
Feb 162026-02-16 00:00:00 UTC2026-02-23 00:00:00 UTC101
Feb 232026-02-23 00:00:00 UTC2026-03-02 00:00:00 UTC000
Mar 22026-03-02 00:00:00 UTC2026-03-09 00:00:00 UTC000
Mar 92026-03-09 00:00:00 UTC2026-03-16 00:00:00 UTC000
Mar 162026-03-16 00:00:00 UTC2026-03-23 00:00:00 UTC000
Mar 232026-03-23 00:00:00 UTC2026-03-30 00:00:00 UTC000
Mar 302026-03-30 00:00:00 UTC2026-04-06 00:00:00 UTC101
Apr 62026-04-06 00:00:00 UTC2026-04-13 00:00:00 UTC101
Apr 132026-04-13 00:00:00 UTC2026-04-20 00:00:00 UTC000
Apr 202026-04-20 00:00:00 UTC2026-04-27 00:00:00 UTC000
Apr 272026-04-27 00:00:00 UTC2026-05-04 00:00:00 UTC000
May 42026-05-04 00:00:00 UTC2026-05-11 00:00:00 UTC000
May 112026-05-11 00:00:00 UTC2026-05-18 00:00:00 UTC000
May 182026-05-18 00:00:00 UTC2026-05-25 00:00:00 UTC000
May 252026-05-25 00:00:00 UTC2026-06-01 00:00:00 UTC000
Jun 12026-06-01 00:00:00 UTC2026-06-08 00:00:00 UTC000
Jun 82026-06-08 00:00:00 UTC2026-06-15 00:00:00 UTC000
Jun 152026-06-15 00:00:00 UTC2026-06-22 00:00:00 UTC000
Jun 222026-06-22 00:00:00 UTC2026-06-29 00:00:00 UTC000
Jun 292026-06-29 00:00:00 UTC2026-07-06 00:00:00 UTC000
Jul 62026-07-06 00:00:00 UTC2026-07-13 00:00:00 UTC000
Jul 132026-07-13 00:00:00 UTC2026-07-20 00:00:00 UTC000
Jul 202026-07-20 00:00:00 UTC2026-07-27 00:00:00 UTC000
Jul 272026-07-27 00:00:00 UTC2026-08-03 00:00:00 UTC000
Aug 32026-08-03 00:00:00 UTC2026-08-10 00:00:00 UTC000
Aug 102026-08-10 00:00:00 UTC2026-08-17 00:00:00 UTC000
Aug 172026-08-17 00:00:00 UTC2026-08-24 00:00:00 UTC000
Aug 242026-08-24 00:00:00 UTC2026-08-31 00:00:00 UTC101

What Hacker News says

5 sources
phenomen·Aug 24, 2026
I tested many open-source and hosted OCR models and Datalab Chandra was the most accurate. It can parse complex layouts, tables, handwriting, and formulas at a fraction of the cost of Claude/Gemini. Local: https://github.com/datalab-to/chandra Hosted: https://www.datalab.to Another decent option is GLM OCR. It's slightly less accurate but faster and cheaper. Local: https://github.com/zai-org/GLM-OCR Hosted: https://docs.z.ai/guides/vlm/glm-ocr Other models such as PaddleOCR, dots.ocr and DeepSeek OCR performed significantly worse.
In OCR It – pull text out of un-copyable documents for your LLM
philipkglass·Apr 6, 2026
At least one of the test questions was just a screen shot from a tweet. It was difficult to read. I'd suggest extracting text from screen shots with OCR. Apple has built-in functionality for this on their operating systems with Live Text. There are strong open source systems based on small vision language models for this, too. The one I have been recommending lately is GLM-OCR: https://github.com/zai-org/GLM-OCR It's fast and can run even on low-resource computers. --- Does this CAPTCHA actually resist computers? I didn't try feeding the questions I got to an LLM, but my sense is that current frontier models could probably pass all of these too. Making generated text pass the pangram test is simple enough for someone actually writing a bot to spin up automated accounts.
In Show HN: I Built Paul Graham's Intellectual Captcha Idea
philipkglass·Apr 2, 2026
If you have a basic ARM MacBook, GLM-OCR is the best single model I have found for OCR with good table extraction/formatting. It's a compact 0.9b parameter model, so it'll run on systems with only 8 GB of RAM. https://github.com/zai-org/GLM-OCR Use mlx-vlm for inference: https://github.com/zai-org/GLM-OCR/blob/main/examples/mlx-de... Then you can run a single command to process your PDF: glmocr parse example.pdf Loading images: example.pdf Found 1 file(s) Starting Pipeline... Pipeline started! GLM-OCR initialized in self-hosted mode Using Pipeline (enable_layout=true)... === Parsing: example.pdf (1/1) === My test document contains scanned pages from a law textbook. It's two columns of text with a lot of footnotes. It took 60 seconds to process 5 pages on a MBP with M4 Max chip. After it's done, you'll have a directory o
Read the rest
utput/example/ that contains .md and .json files. The .md file will contain a markdown rendition of the complete document. The .json file will contain individual labeled regions from the document along with their transcriptions. If you get all the JSON objects with "label": "table" from the JSON file, you can get an HTML-formatted table from each "content" section of these objects. It might still be inaccurate -- I don't know how challenging your original tables are -- but it shouldn't be terribly slow. The tables it produced for me were good. I have also built more complex work flows that use a mixture of OCR-specialized models and general purpose VLM models like Qwen 3.5, along with software to coordinate and reconcile operations, but GLM-OCR by itself is the best first thing to try locally.
In Google releases Gemma 4 open models
xml·Feb 16, 2026
You can still be excited! Recently, GLM-OCR was released, which is a relatively small OCR model (2.5 GB unquantized) that can run on CPU with good quality. I've been using it to digitize various hand-written notes and all my shopping receipts this week. https://github.com/zai-org/GLM-OCR (Shameless plug: I also maintain a simplified version of GLM-OCR without dependency on the transformers library, which makes it much easier to install: https://github.com/99991/Simple-GLM-OCR/)
In Rolling your own serverless OCR in 40 lines of code

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