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Navigating Document Parsing Across Digital and Camera-Captured Documents
NaviDC-OCR is a lightweight (~1.2B parameters), open-source Vision-Language Model designed specifically for document parsing.
Unlike existing methods that mainly target either digital documents or camera-captured documents, NaviDC-OCR unifies both scenarios within a single framework.
Compared with previous document parsing models, NaviDC-OCR introduces
These techniques enable NaviDC-OCR to achieve state-of-the-art performance on both digital and camera-captured document benchmarks while remaining lightweight enough for practical deployment.
NaviDC-OCR achieves state-of-the-art performance on multiple public document parsing benchmarks.
To evaluate the model's ability to understand complex document deformations, we conduct a visual evaluation on the public dewarping datasets DocUNet and DIR300, with representative results shown in Figure. NaviDC-OCR directly performs layout and content parsing on distorted documents without dewarping preprocessing or a dedicated rectification model, demonstrating robust parsing under complex geometric deformations.
If you would like to perform complete document parsing, please refer to our GitHub repository: https://github.com/caipeng328/NaviDC-OCR.
Thanks to Nandraj for the GGUF conversion and llama.cpp support! π NaviDC-OCR-GGUF
NaviDC-OCR is built upon
We sincerely thank these excellent open-source projects.
If you have any questions, feel free to open an issue or contact us.
pip install transformers torch pillowimport htmlimport itertoolsimport jsonimport refrom dataclasses import dataclass
from PIL import Imageimport torchfrom transformers import AutoProcessor, AutoModel
@dataclassclass ContentBlock: type: str bbox: list[float] angle: int | None = None content: str | None = None
@dataclassclass TableCell: text: str start_row_offset_idx: int end_row_offset_idx: int start_col_offset_idx: int end_col_offset_idx: int row_span: int = 1 col_span: int = 1
OTSL_NL = "<nl>"OTSL_FCEL = "<fcel>"OTSL_ECEL = "<ecel>"OTSL_LCEL = "<lcel>"OTSL_UCEL = "<ucel>"OTSL_XCEL = "<xcel>"OTSL_TOKENS = [OTSL_NL, OTSL_FCEL, OTSL_ECEL, OTSL_LCEL, OTSL_UCEL, OTSL_XCEL]
def _otsl_extract_tokens_and_text(text: str): pattern = "(" + "|".join(map(re.escape, OTSL_TOKENS)) + ")" tokens = re.findall(pattern, text) parts = [part for part in re.split(pattern, text) if part.strip()] return tokens, parts
def _count_right(rows, row_idx, col_idx, tokens): span = 0 while col_idx < len(rows[row_idx]) and rows[row_idx][col_idx] in tokens: span += 1 col_idx += 1 return span
def _count_down(rows, row_idx, col_idx, tokens): span = 0 while row_idx < len(rows) and col_idx < len(rows[row_idx]) and rows[row_idx][col_idx] in tokens: span += 1 row_idx += 1 return span
def _otsl_parse_texts(parts, tokens): rows = [list(row) for is_nl, row in itertools.groupby(tokens, lambda token: token == OTSL_NL) if not is_nl] if not rows: return [], []
max_cols = max(len(row) for row in rows) for row in rows: row.extend([OTSL_ECEL] * (max_cols - len(row)))
cells = [] row_idx = 0 col_idx = 0 for idx, part in enumerate(parts): if part in (OTSL_FCEL, OTSL_ECEL): cell_text = "" right_offset = 1 if part != OTSL_ECEL and idx + 1 < len(parts) and parts[idx + 1] not in OTSL_TOKENS: cell_text = parts[idx + 1].strip() right_offset = 2
next_right = parts[idx + right_offset] if idx + right_offset < len(parts) else "" next_bottom = rows[row_idx + 1][col_idx] if row_idx + 1 < len(rows) and col_idx < len(rows[row_idx + 1]) else "" col_span = 1 + (_count_right(rows, row_idx, col_idx + 1, {OTSL_LCEL, OTSL_XCEL}) if next_right in {OTSL_LCEL, OTSL_XCEL} else 0) row_span = 1 + (_count_down(rows, row_idx + 1, col_idx, {OTSL_UCEL, OTSL_XCEL}) if next_bottom in {OTSL_UCEL, OTSL_XCEL} else 0) cells.append(TableCell( text=cell_text, row_span=row_span, col_span=col_span, start_row_offset_idx=row_idx, end_row_offset_idx=row_idx + row_span, start_col_offset_idx=col_idx, end_col_offset_idx=col_idx + col_span, )) if part in (OTSL_FCEL, OTSL_ECEL, OTSL_LCEL, OTSL_UCEL, OTSL_XCEL): col_idx += 1 elif part == OTSL_NL: row_idx += 1 col_idx = 0 return cells, rows
def convert_otsl_to_html(otsl_content: str) -> str: if otsl_content.startswith("<table") and otsl_content.endswith("</table>"): return otsl_content
tokens, parts = _otsl_extract_tokens_and_text(otsl_content) cells, rows = _otsl_parse_texts(parts, tokens) if not cells or not rows: return ""
grid = [[None for _ in range(len(rows[0]))] for _ in range(len(rows))] for cell in cells: for row_idx in range(cell.start_row_offset_idx, min(cell.end_row_offset_idx, len(rows))): for col_idx in range(cell.start_col_offset_idx, min(cell.end_col_offset_idx, len(rows[0]))): grid[row_idx][col_idx] = cell
html_rows = [] for row_idx, row in enumerate(grid): html_rows.append("<tr>") for col_idx, cell in enumerate(row): if cell is None or cell.start_row_offset_idx != row_idx or cell.start_col_offset_idx != col_idx: continue attrs = "" if cell.row_span > 1: attrs += f' rowspan="{cell.row_span}"' if cell.col_span > 1: attrs += f' colspan="{cell.col_span}"' html_rows.append(f"<td{attrs}>{html.escape(cell.text.strip())}</td>") html_rows.append("</tr>") return "<table>" + "".join(html_rows) + "</table>"
def post_process(blocks: list[ContentBlock]) -> list[ContentBlock]: for block in blocks: if block.type == "table" and block.content: block.content = convert_otsl_to_html(block.content) elif block.type == "equation" and block.content: content = block.content.strip() content = content.removeprefix("\\[").removesuffix("\\]").strip() if not (content.startswith("$") and content.endswith("$")): content = f"$${content}$$" block.content = content return [block for block in blocks if block.type != "equation_block"]
def infer(image: Image.Image, prompt: str) -> str: messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": prompt}, ]}, ] chat_prompt = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) inputs = processor( text=[chat_prompt], images=[image.convert("RGB")], padding=True, return_tensors="pt", ).to(device=model.device, dtype=model.dtype) output_ids = model.generate( **inputs, use_cache=True, max_new_tokens=4096, do_sample=False, ) output_ids = output_ids.cpu().tolist()[0][len(inputs.input_ids[0]):] return processor.batch_decode( [output_ids], skip_special_tokens=True, clean_up_tokenization_spaces=False, )[0].strip()
processor = AutoProcessor.from_pretrained("StarDoc-AI/NaviDC-OCR", trust_remote_code=True, use_fast=True)model = AutoModel.from_pretrained( "StarDoc-AI/NaviDC-OCR", trust_remote_code=True, torch_dtype=torch.bfloat16,).cuda().eval()
# textimage=Image.open("./assets/text.png").convert("RGB")raw_text = infer(image, "Please output the text content from the image.")print(raw_text.strip())
# tableimage=Image.open("./assets/table.png").convert("RGB")raw_otsl = infer(image, "This is the image of a table. Please output the table in OTSL format.")print(convert_otsl_to_html(raw_otsl))
# formula image=Image.open("./assets/formula.png").convert("RGB")raw_formula = infer(image, "Please write out the expression of the formula in the image using LaTeX format.")formula_block = ContentBlock("equation", [0.0, 0.0, 1.0, 1.0], content=raw_formula)formula = post_process([formula_block])[0].contentprint(formula)
#codeimage=Image.open("./assets/code.png").convert("RGB")raw_code = infer(image,"The image contains a code snippet, please output the parsing result.")print(raw_code.strip())
# layoutimage=Image.open("./assets/layout.jpg").convert("RGB")image = image.resize((1036, 1036), Image.Resampling.BICUBIC)raw_layout = infer(image, "Analyze the image layout.")print(raw_layout.strip())
# Distorted document layoutlayout_image = Image.open("./assets/layout_distorted.jpg").convert("RGB")layout_image = layout_image.resize((1036, 1036), Image.Resampling.BICUBIC)raw_layout = infer(layout_image, "\nMulti-point Layout Segmentation Analysis.")print(raw_layout.strip())
#scientific figureimage=Image.open("./assets/scientific_figure.png").convert("RGB")raw_scientific_figure = infer(image, "This is a scientific figure. Please extract the table implied by this figure.")print(convert_otsl_to_html(raw_scientific_figure))@article{navidc_ocr, title={NaviDC-OCR: Navigating Document Parsing Across Digital and Camera-Captured Documents}, author={Cai, Peng and Zou, Zhaofan and Liu, Shifa and Wang, Yikun and Tang, Jiawei and Yang, Kaicheng and Tong, Meng and He, Zhongjiang and Sun, Hao}, journal={arXiv preprint arXiv:2608.12898}, year={2026}}