dhchoi

bandit-ocr

Model details

Table

Base model	dots-studio/dots.mocr at revision `e539fbb52280393adc081b289ec597430a0f9031`
Parameters	1.7B (1.2B language decoder + 0.4B vision tower)
Adaptation	LoRA, rank 64, alpha 128, dropout 0.05, on the language projections and the vision tower
Weights	root: the adapter folded in at scale 0.75, a plain bfloat16 checkpoint, no PEFT needed
Adapter	`lora/`: the same LoRA unmerged, 580 MB, to apply at scale 0.75 yourself
Files	2 safetensors shards at the root, about 6.1 GB, plus the adapter
Checkpoint	t3_full step 56000 of the full training run
Trained on	229,356 annotated pages of Korean historical documents
Input	one page image, 3,136 to 11,289,600 pixels after `smart_resize`
Output	JSON array of column regions in reading order
Author	Donghyeok Choi, Department of History, Hong Kong Baptist University
Project	https://bandit.dhchoi.net

What it returns

One JSON array, one object per printed column, in reading order:

json
[
  {"bbox": [2205, 354, 2350, 3406], "category": "Text", "text": "李敷金輅孫興宗沈孝生高呂張至和咸傅霖韓尙敬黃居正"},
  {"bbox": [2057, 366, 2193, 3430], "category": "Text", "text": "任彦忠張思靖閔汝翼等大小臣僚及閑良耆老等奉國寶詣"}
]

Those are the first two of the fifteen columns of one Sillok page from the test split, 2400 x 3744 pixels, on its 2408 x 3752 input grid.

Reading order is the convention of the printed page: column slots right to left, each slot top to bottom, and the two sub-columns of an interlinear note (세주) right to left. An interlinear note is a column region like any other, at about half width, with no markup in its text.

bbox is [x1, y1, x2, y2] in the resized input grid, not in original pixels. Map it back with the same smart_resize the processor used:

python
from qwen_vl_utils.vision_process import smart_resize

grid_h, grid_w = smart_resize(height, width, min_pixels=3136, max_pixels=11289600)
x1 = round(x1 * width / grid_w)
y1 = round(y1 * height / grid_h)

Usage

Transformers

The model ships the base's remote code, so trust_remote_code=True is required. It was trained and evaluated against transformers==4.51.3; 4.52 and later change the Qwen2.5-VL processor contract the custom DotsVLProcessor was written for.

python
import json

import torch
from PIL import Image
from qwen_vl_utils import process_vision_info
from qwen_vl_utils.vision_process import smart_resize
from transformers import AutoModelForCausalLM, AutoProcessor

MODEL = "dhchoi/bandit-ocr"
PROMPT = (
    "Please output the layout information from the PDF image, including each layout element's "
    "bbox, its category, and the corresponding text content within the bbox.\n\n"
    "1. Bbox format: [x1, y1, x2, y2]\n\n"
    "2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', "
    "'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', "
    "'Title'].\n\n"
    "3. Text Extraction & Formatting Rules:\n"
    "    - Picture: For the 'Picture' category, the text field should be omitted.\n"
    "    - Formula: Format its text as LaTeX.\n"
    "    - Table: Format its text as HTML.\n"
    "    - All Others (Text, Title, etc.): Format their text as Markdown.\n\n"
    "4. Constraints:\n"
    "    - The output text must be the original text from the image, with no translation.\n"
    "    - All layout elements must be sorted according to human reading order.\n\n"
    "5. Final Output: The entire output must be a single JSON object.\n"
)
MIN_PIXELS, MAX_PIXELS = 3136, 11289600

model = AutoModelForCausalLM.from_pretrained(
    MODEL, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto"
)
processor = AutoProcessor.from_pretrained(MODEL, trust_remote_code=True)

path = "page.jpg"
with Image.open(path) as im:
    width, height = im.size
grid_h, grid_w = smart_resize(height, width, min_pixels=MIN_PIXELS, max_pixels=MAX_PIXELS)

messages = [{"role": "user", "content": [
    {"type": "image", "image": path, "min_pixels": MIN_PIXELS, "max_pixels": MAX_PIXELS},
    {"type": "text", "text": PROMPT},
]}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
images, videos = process_vision_info(messages)
images = [im.resize((grid_w, grid_h), Image.Resampling.LANCZOS) for im in images]
inputs = processor(text=[text], images=images, videos=videos, padding=True,
                   return_tensors="pt").to(model.device)

with torch.inference_mode():
    out = model.generate(
        **inputs,
        max_new_tokens=8192,
        do_sample=False,
        eos_token_id=processor.tokenizer.convert_tokens_to_ids("<|endofassistant|>"),
        suppress_tokens=[151643, 151672],
    )
answer = processor.batch_decode(out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]
regions = json.loads(answer)

vLLM

vLLM 0.21.0 serves the merged weights with no conversion; it resolves DotsOCRForCausalLM from the remote code itself.

bash
vllm serve dhchoi/bandit-ocr \
  --trust-remote-code --dtype bfloat16 --max-model-len 24576 \
  --limit-mm-per-prompt '{"image": 1}' \
  --mm-processor-kwargs '{"max_pixels": 11289600, "min_pixels": 3136}'

Send the image as a data URL with the prompt above, temperature 0, max_tokens 8192, and the two decode settings of the section below: "stop_token_ids": [151673] and "logit_bias": {"151643": -100, "151672": -100}.

The LoRA adapter

lora/ holds the same fine-tune unmerged, for composing onto the base yourself, changing the scale or training further. The delta is scaled by 0.75 before it is applied: that scale is part of the released model, not a training artefact, and every number below was measured with it.

python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained(
    "dots-studio/dots.mocr", revision="e539fbb52280393adc081b289ec597430a0f9031",
    trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto",
)
model = PeftModel.from_pretrained(base, "dhchoi/bandit-ocr", subfolder="lora")

for module in model.modules():                  # every LoRA layer scales its delta by scaling[name]
    scaling = getattr(module, "scaling", None)
    if isinstance(scaling, dict):
        for name in scaling:
            scaling[name] *= 0.75

model = model.merge_and_unload()

That reproduces the weights at the root of this repository. Prompt, decode and rescale boxes the same way, and set the stop id yourself, because the composed model inherits the base's generation config.

Applied at its trained scale of 1.0 the adapter is a different, measurably worse model: on the same 540-page validation set an earlier checkpoint of this run read at 0.0656 page CER at scale 1.0 against 0.0376 at scale 0.75, both under the stop rule of the day.

Decoding: stop on one token, suppress two

This matters more than any other setting here. The training target ends with <|endofassistant|> (151673) and nothing else, but the base generation config also treats <|endoftext|> (151643) and <|assistant|> (151672) as stop ids. The fine-tune sometimes emits <|endoftext|> in the middle of a column at a hard glyph, and a page that stops there is truncated or fails to parse.

generation_config.json at the root of this repository already sets eos_token_id to 151673 alone. If you compose the adapter onto the base yourself, set it there: the base's config stops on all three.
Suppress 151643 and 151672 during decoding as well (suppress_tokens in generate, a -100 logit_bias through an OpenAI-compatible server).

Measured on 540 validation pages, this rule takes the released checkpoint from 0.0517 page CER with 2 pages failing to parse down to 0.0271 with none, weights unchanged. On the 16 pages of a diagnostic cohort that stopped early at a neighbouring checkpoint, micro CER fell from 0.652 to 0.027 while six control pages stayed put. Every number reported below was measured under this rule.

Evaluation

The whole held-out test split of the training corpus, 14,376 pages read through vLLM with the decode rule above. Regions are matched one to one at IoU 0.5; CER is over matched regions (region CER) and over the concatenated reading-order text of the page (page CER). The layout row scores every test page that has ground-truth geometry; the text row scores the pages that also carry ground-truth text.

Table with columns: Pages, P, R, F1, mean IoU, region CER, page CER
	Pages	P	R	F1	mean IoU	region CER	page CER
layout ground truth	13,636	0.9897	0.9805	0.9851	0.9580	0.0256	0.0235
text ground truth	12,935	0.9909	0.9804	0.9856

By script style, on the text ground truth:

Table with columns: Pages, P, R, F1, mean IoU, region CER, page CER
	Pages	P	R	F1	mean IoU	region CER	page CER
haeseo 해서 (standard)	5,573	0.9894	0.9713	0.9803	0.9488	0.0309	0.0252
haengseo 행서 (semi-cursive)	1,558	0.9692	0.9779	0.9735

By collection:

Table with columns: Pages, P, R, F1, mean IoU, region CER, page CER
	Pages	P	R	F1	mean IoU	region CER	page CER
AI Hub 234 (고서 한자 인식)	2,578	0.9738	0.9801	0.9769	0.9485	0.0312	0.0404
AI Hub 603 (고서 한자 인식 OCR)	2,391	0.9874	0.9533	0.9700

Against the baselines, same pages, same ground truth

Table with columns: System, layout F1, page CER
System	layout F1	page CER
Bandit OCR (this model)	0.9851	0.0195
AI Hub ResNet pipeline	0.9595	0.1436
NDLkotenOCR	0.8880	0.2398
dots.ocr, base	0.1602	0.1497

The base dots.ocr layout F1 is a unit mismatch rather than a reading failure: the base answers with about 3 regions where the ground truth has 15, so its page CER is the comparable figure. A base dots.mocr row is not measured yet.

Out of domain

1,000 pages of Chinese historical print (HisDoc1B), a tradition the model never saw:

Table with columns: Pages, P, R, F1, mean IoU, region CER, page CER
	Pages	P	R	F1	mean IoU	region CER	page CER
HisDoc1B	1,000	0.6773	0.9512	0.7912	0.9165	0.2144	0.2241

Recall 0.95 says the columns are found and page CER 0.22 is the transfer number. The precision is over-segmentation: the model returns 28,983 regions against the ground truth's 20,638, and a quarter of its regions hold five characters or fewer.

Training data

229,356 pages and 3,023,103 column regions, split off a corpus of four Korean collections by book volume so that no volume straddles train and test (seed 20260828, 90/5/5):

조선왕조실록 Veritable Records of the Joseon Dynasty, National Institute of Korean History: 99,764 training pages. Column boxes come from a character detector run over each page, and the characters of each column come from aligning the article's transcription against that detection.
AI Hub 고서 한자 인식 datasets 234, 603 and 71294: 129,592 training pages of woodblock prints and manuscripts in five script styles, with character-level boxes grouped into columns.

The corpus itself is not published and cannot be: the AI Hub data may not be redistributed and the Sillok images are copyrighted by the National Institute of Korean History. Only the weights are released.

The text is the printed form: Hanja, the ○ article marker, □ for a glyph with no code point, and note text. Editorial punctuation, bracket marks and collation insertions are removed.

Training procedure

One run on H200 GPUs: bfloat16, gradient checkpointing, effective batch 8 (per-device batch 1 with gradient accumulation), AdamW, cosine schedule, warmup ratio 0.03, weight decay 0.01, seed 3407, flash_attention_2, one page per sample at up to 11,289,600 pixels, batches grouped by length. The learning rate was 2e-4 for the first 32,000 steps and 5e-5 from there, on a cosine schedule spanning the remaining steps; step 56000 is about 1.95 epochs over the 229,356-page training split.

Checkpoints were evaluated every 4,000 steps by generating a 540-page validation set and scoring it the way the test split is scored, and the released checkpoint is the best of that curve by page CER. The curve is flat from step 44,000 on, within 0.0036 CER of the selected point.

The LoRA delta is applied at scale 0.75. That is not a training setting: scaling the merged delta below 1.0 was measured to read better than the trained scale, and it is folded into the merged weights because a server applies no runtime scale.

Limitations

Cursive (초서) is the weak style: page CER 0.2063 against 0.0252 on standard script, and the lowest recall in the corpus (0.8274). The test split holds only 88 cursive pages, so that number is itself uncertain.
The clerical (예서) row is 15 pages. Read it as an indication, not a score.
The Sillok layout score is not independent. The v3 Sillok column boxes were drawn by HRCenterNet, the AI Hub baseline's own detector, and this model was trained on them. A 100-page human-corrected gold set confirms the column inventory (304 boxes drawn blind against 304), so recall and F1 on Sillok are meaningful, but the mean IoU on Sillok pages carries that circularity.
Out of domain it over-segments. On Chinese material it splits small text, headings and notes into several regions where the ground truth keeps one column, which costs precision, not reading.
It is a column reader, not a general document parser. The prompt is the base's full layout prompt, but the fine-tune answers with Text column regions; tables, formulas and figures are not part of the training target and the base's behaviour on them has been trained away.
No safety or content filtering of any kind. It transcribes what is on the page.

A note on the remote code

The custom modelling files are the base's, with four idempotent patches this project needs on its hosts, all of them to how attention is chosen and imported, none of them to the architecture or the weights:

the flash-attn import is wrapped in a try/except, so the file imports on a host without it;
DotsVisionTransformer declares _supports_sdpa and _supports_flash_attn, which transformers checks before it will give a sub-model sdpa;
the sdpa path skips its all-true attention mask when the batch holds a single image, which is exact and avoids a mask that costs about 95 GB on a full page;
DotsVLProcessor passes video_processor to the Qwen2.5-VL processor by keyword, which transformers 4.51 otherwise reads as chat_template.

config.json asks the vision tower for flash_attention_2. Without flash-attn installed it falls back to eager attention, which is quadratic in the number of patches; set vision_config.attn_implementation to sdpa in that case.

Licence and attribution

Built with dots.mocr.

This model is a fine-tune of dots.mocr, copyright Xingyin Information Technology (Shanghai) Co., Ltd, used under the dots.mocr LICENSE AGREEMENT. A copy of that agreement and the base NOTICE ship in this repository (clauses 7.1 and 7.2), the notices in them are unchanged, and this card displays the attribution statement clause 7.3 requires. Nothing here implies endorsement by the licensor, and no name in this project is a licensor trade name (clause 4.4). The weights are released on the terms the base is released under; where the agreement and the metadata tag differ, the agreement is the document that ships.

The training data is licensed separately and is not redistributed; see the section above.

Citation

bibtex
@inproceedings{choi2026bandit,
  title     = {Bandit: A Human-in-the-Loop OCR System for East Asian Historical Documents
               with Vision-Language Model Fine-tuning},
  author    = {Choi, Donghyeok},
  booktitle = {Japanese Association for Digital Humanities (JADH) 2026},
  year      = {2026}
}

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Model Details

Model Provider

dhchoi

Model Tree

Base

dots-studio/dots.mocr

Fine-tuned

this model

Input Modalities

TextImage

Output Modalities

Text

Supported Functionality

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Model details

Table

Base model	dots-studio/dots.mocr at revision `e539fbb52280393adc081b289ec597430a0f9031`
Parameters	1.7B (1.2B language decoder + 0.4B vision tower)
Adaptation	LoRA, rank 64, alpha 128, dropout 0.05, on the language projections and the vision tower
Weights	root: the adapter folded in at scale 0.75, a plain bfloat16 checkpoint, no PEFT needed
Adapter	`lora/`: the same LoRA unmerged, 580 MB, to apply at scale 0.75 yourself
Files	2 safetensors shards at the root, about 6.1 GB, plus the adapter
Checkpoint	t3_full step 56000 of the full training run
Trained on	229,356 annotated pages of Korean historical documents
Input	one page image, 3,136 to 11,289,600 pixels after `smart_resize`
Output	JSON array of column regions in reading order
Author	Donghyeok Choi, Department of History, Hong Kong Baptist University
Project	https://bandit.dhchoi.net

What it returns

One JSON array, one object per printed column, in reading order:

json
[
  {"bbox": [2205, 354, 2350, 3406], "category": "Text", "text": "李敷金輅孫興宗沈孝生高呂張至和咸傅霖韓尙敬黃居正"},
  {"bbox": [2057, 366, 2193, 3430], "category": "Text", "text": "任彦忠張思靖閔汝翼等大小臣僚及閑良耆老等奉國寶詣"}
]

Those are the first two of the fifteen columns of one Sillok page from the test split, 2400 x 3744 pixels, on its 2408 x 3752 input grid.

bbox is [x1, y1, x2, y2] in the resized input grid, not in original pixels. Map it back with the same smart_resize the processor used:

python
from qwen_vl_utils.vision_process import smart_resize

grid_h, grid_w = smart_resize(height, width, min_pixels=3136, max_pixels=11289600)
x1 = round(x1 * width / grid_w)
y1 = round(y1 * height / grid_h)

Usage

Transformers

python
import json

import torch
from PIL import Image
from qwen_vl_utils import process_vision_info
from qwen_vl_utils.vision_process import smart_resize
from transformers import AutoModelForCausalLM, AutoProcessor

MODEL = "dhchoi/bandit-ocr"
PROMPT = (
    "Please output the layout information from the PDF image, including each layout element's "
    "bbox, its category, and the corresponding text content within the bbox.\n\n"
    "1. Bbox format: [x1, y1, x2, y2]\n\n"
    "2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', "
    "'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', "
    "'Title'].\n\n"
    "3. Text Extraction & Formatting Rules:\n"
    "    - Picture: For the 'Picture' category, the text field should be omitted.\n"
    "    - Formula: Format its text as LaTeX.\n"
    "    - Table: Format its text as HTML.\n"
    "    - All Others (Text, Title, etc.): Format their text as Markdown.\n\n"
    "4. Constraints:\n"
    "    - The output text must be the original text from the image, with no translation.\n"
    "    - All layout elements must be sorted according to human reading order.\n\n"
    "5. Final Output: The entire output must be a single JSON object.\n"
)
MIN_PIXELS, MAX_PIXELS = 3136, 11289600

model = AutoModelForCausalLM.from_pretrained(
    MODEL, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto"
)
processor = AutoProcessor.from_pretrained(MODEL, trust_remote_code=True)

path = "page.jpg"
with Image.open(path) as im:
    width, height = im.size
grid_h, grid_w = smart_resize(height, width, min_pixels=MIN_PIXELS, max_pixels=MAX_PIXELS)

messages = [{"role": "user", "content": [
    {"type": "image", "image": path, "min_pixels": MIN_PIXELS, "max_pixels": MAX_PIXELS},
    {"type": "text", "text": PROMPT},
]}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
images, videos = process_vision_info(messages)
images = [im.resize((grid_w, grid_h), Image.Resampling.LANCZOS) for im in images]
inputs = processor(text=[text], images=images, videos=videos, padding=True,
                   return_tensors="pt").to(model.device)

with torch.inference_mode():
    out = model.generate(
        **inputs,
        max_new_tokens=8192,
        do_sample=False,
        eos_token_id=processor.tokenizer.convert_tokens_to_ids("<|endofassistant|>"),
        suppress_tokens=[151643, 151672],
    )
answer = processor.batch_decode(out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]
regions = json.loads(answer)

vLLM

vLLM 0.21.0 serves the merged weights with no conversion; it resolves DotsOCRForCausalLM from the remote code itself.

bash
vllm serve dhchoi/bandit-ocr \
  --trust-remote-code --dtype bfloat16 --max-model-len 24576 \
  --limit-mm-per-prompt '{"image": 1}' \
  --mm-processor-kwargs '{"max_pixels": 11289600, "min_pixels": 3136}'

The LoRA adapter

python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained(
    "dots-studio/dots.mocr", revision="e539fbb52280393adc081b289ec597430a0f9031",
    trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto",
)
model = PeftModel.from_pretrained(base, "dhchoi/bandit-ocr", subfolder="lora")

for module in model.modules():                  # every LoRA layer scales its delta by scaling[name]
    scaling = getattr(module, "scaling", None)
    if isinstance(scaling, dict):
        for name in scaling:
            scaling[name] *= 0.75

model = model.merge_and_unload()

Decoding: stop on one token, suppress two

generation_config.json at the root of this repository already sets eos_token_id to 151673 alone. If you compose the adapter onto the base yourself, set it there: the base's config stops on all three.
Suppress 151643 and 151672 during decoding as well (suppress_tokens in generate, a -100 logit_bias through an OpenAI-compatible server).

Evaluation

Table with columns: Pages, P, R, F1, mean IoU, region CER, page CER
	Pages	P	R	F1	mean IoU	region CER	page CER
layout ground truth	13,636	0.9897	0.9805	0.9851	0.9580	0.0256	0.0235
text ground truth	12,935	0.9909	0.9804	0.9856

By script style, on the text ground truth:

Table with columns: Pages, P, R, F1, mean IoU, region CER, page CER
	Pages	P	R	F1	mean IoU	region CER	page CER
haeseo 해서 (standard)	5,573	0.9894	0.9713	0.9803	0.9488	0.0309	0.0252
haengseo 행서 (semi-cursive)	1,558	0.9692	0.9779	0.9735

By collection:

Table with columns: Pages, P, R, F1, mean IoU, region CER, page CER
	Pages	P	R	F1	mean IoU	region CER	page CER
AI Hub 234 (고서 한자 인식)	2,578	0.9738	0.9801	0.9769	0.9485	0.0312	0.0404
AI Hub 603 (고서 한자 인식 OCR)	2,391	0.9874	0.9533	0.9700

Against the baselines, same pages, same ground truth

Table with columns: System, layout F1, page CER
System	layout F1	page CER
Bandit OCR (this model)	0.9851	0.0195
AI Hub ResNet pipeline	0.9595	0.1436
NDLkotenOCR	0.8880	0.2398
dots.ocr, base	0.1602	0.1497

Out of domain

1,000 pages of Chinese historical print (HisDoc1B), a tradition the model never saw:

Table with columns: Pages, P, R, F1, mean IoU, region CER, page CER
	Pages	P	R	F1	mean IoU	region CER	page CER
HisDoc1B	1,000	0.6773	0.9512	0.7912	0.9165	0.2144	0.2241

Training data

229,356 pages and 3,023,103 column regions, split off a corpus of four Korean collections by book volume so that no volume straddles train and test (seed 20260828, 90/5/5):

조선왕조실록 Veritable Records of the Joseon Dynasty, National Institute of Korean History: 99,764 training pages. Column boxes come from a character detector run over each page, and the characters of each column come from aligning the article's transcription against that detection.
AI Hub 고서 한자 인식 datasets 234, 603 and 71294: 129,592 training pages of woodblock prints and manuscripts in five script styles, with character-level boxes grouped into columns.

The text is the printed form: Hanja, the ○ article marker, □ for a glyph with no code point, and note text. Editorial punctuation, bracket marks and collation insertions are removed.

Training procedure

Limitations

Cursive (초서) is the weak style: page CER 0.2063 against 0.0252 on standard script, and the lowest recall in the corpus (0.8274). The test split holds only 88 cursive pages, so that number is itself uncertain.
The clerical (예서) row is 15 pages. Read it as an indication, not a score.
The Sillok layout score is not independent. The v3 Sillok column boxes were drawn by HRCenterNet, the AI Hub baseline's own detector, and this model was trained on them. A 100-page human-corrected gold set confirms the column inventory (304 boxes drawn blind against 304), so recall and F1 on Sillok are meaningful, but the mean IoU on Sillok pages carries that circularity.
Out of domain it over-segments. On Chinese material it splits small text, headings and notes into several regions where the ground truth keeps one column, which costs precision, not reading.
It is a column reader, not a general document parser. The prompt is the base's full layout prompt, but the fine-tune answers with Text column regions; tables, formulas and figures are not part of the training target and the base's behaviour on them has been trained away.
No safety or content filtering of any kind. It transcribes what is on the page.

A note on the remote code

the flash-attn import is wrapped in a try/except, so the file imports on a host without it;
DotsVisionTransformer declares _supports_sdpa and _supports_flash_attn, which transformers checks before it will give a sub-model sdpa;
the sdpa path skips its all-true attention mask when the batch holds a single image, which is exact and avoids a mask that costs about 95 GB on a full page;
DotsVLProcessor passes video_processor to the Qwen2.5-VL processor by keyword, which transformers 4.51 otherwise reads as chat_template.

Licence and attribution

Built with dots.mocr.

The training data is licensed separately and is not redistributed; see the section above.

Citation

bibtex
@inproceedings{choi2026bandit,
  title     = {Bandit: A Human-in-the-Loop OCR System for East Asian Historical Documents
               with Vision-Language Model Fine-tuning},
  author    = {Choi, Donghyeok},
  booktitle = {Japanese Association for Digital Humanities (JADH) 2026},
  year      = {2026}
}

bandit-ocr

README

Model details

What it returns

Usage

Transformers

vLLM

The LoRA adapter

Decoding: stop on one token, suppress two

Evaluation

Against the baselines, same pages, same ground truth

Out of domain

Training data

Training procedure

Limitations

A note on the remote code

Licence and attribution

Citation

Explore FriendliAI today

README

Model details

What it returns

Usage

Transformers

vLLM

The LoRA adapter

Decoding: stop on one token, suppress two

Evaluation

Against the baselines, same pages, same ground truth

Out of domain

Training data

Training procedure

Limitations

A note on the remote code

Licence and attribution

Citation