BLSTM for handwriting (1)
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of Bern,IDSIA,Technical University of Munich
- Organisation type
- Academia,Academia,Academia
- Country
- Switzerland, Germany
- Published
- 23 September 2007
- Authors
- M Liwicki, A Graves, S Fernàndez
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Character recognition (OCR), Image classification
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 405,478 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 287
Sources
Where this record came from and when it was last checked.
- Reference
- A Novel Approach to On-Line Handwriting Recognition Based on Bidirectional Long Short-Term Memory Networks
- Last updated
- 11 February 2026
What the numbers mean
About this model
BLSTM for handwriting (1) was published by University of Bern,IDSIA,Technical University of Munich, in Switzerland, in September 2007. academia,Academia,Academia is the category the publisher falls under.
It works in Vision, and is recorded as doing character recognition (OCR), Image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The training set ran to roughly 405,478 tokens.
Its inclusion criterion is sOTA improvement.
Answers
BLSTM for handwriting (1) — common questions
How many parameters does BLSTM for handwriting (1) have?
No parameter count has been published for BLSTM for handwriting (1), which is why no memory or speed figure appears on this page.
Who created BLSTM for handwriting (1)?
BLSTM for handwriting (1) was published by University of Bern,IDSIA,Technical University of Munich, based in Switzerland, categorised as academia,Academia,Academia.
When was BLSTM for handwriting (1) released?
BLSTM for handwriting (1) was published in September 2007. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is BLSTM for handwriting (1) used for?
BLSTM for handwriting (1) works in Vision, and is recorded as handling character recognition (OCR), Image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run BLSTM for handwriting (1)?
None. BLSTM for handwriting (1) is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is BLSTM for handwriting (1) open source?
The licensing for BLSTM for handwriting (1) was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.