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import threading
from dataclasses import dataclass, field
from typing import Any
from PIL import Image
from ocr_studio.config import (
LANGUAGE_BOTH,
LANGUAGE_EN,
LANGUAGE_FA,
MAX_NEW_TOKENS,
MODE_COMPARE,
MODE_DOCUMENT,
MODE_FIELDS,
MODE_MARKDOWN,
MODE_PRECISE,
MODE_TABLE,
OCR_MAX_PIXELS,
SPOTTING_MAX_PIXELS,
SPOTTING_UPSCALE_THRESHOLD,
)
from ocr_studio.spotting import TextSpan, parse_spans, spans_to_text, strip_special_tokens
from ocr_studio.tiling import iter_tiles, join_tile_text, merge_tile_spans, offset_spans, should_tile
TASK_OCR = "ocr"
TASK_SPOTTING = "spotting"
TASK_TABLE = "table"
OFFICIAL_PROMPTS = {
TASK_OCR: "OCR:",
TASK_SPOTTING: "Spotting:",
TASK_TABLE: "Table Recognition:",
}
LANGUAGE_HINTS = {
LANGUAGE_FA: "The document is in Persian (Farsi). Preserve Persian letters, digits, and RTL order.\n",
LANGUAGE_EN: "The document is in English.\n",
LANGUAGE_BOTH: "The document mixes Persian (Farsi) and English. Preserve both scripts.\n",
"auto": "The document may be Persian (Farsi), English, or mixed.\n",
}
TASK_INSTRUCTIONS = {
MODE_DOCUMENT: "",
MODE_PRECISE: "",
MODE_TABLE: "Recover every table with cell structure.\n",
MODE_MARKDOWN: "Transcribe as Markdown with headings, lists, and tables where they appear.\n",
MODE_FIELDS: "Extract every labeled field as a line in the form 'label: value'. Include names, dates, amounts, IDs, and addresses.\n",
MODE_COMPARE: "",
}
@dataclass
class PageInference:
raw_text: str
display_text: str
spans: list[TextSpan]
task: str
tile_count: int = 1
alt_text: str = ""
@dataclass
class BatchInference:
pages: list[PageInference] = field(default_factory=list)
compare: bool = False
def build_prompt(mode: str, language_key: str, task: str) -> str:
hint = LANGUAGE_HINTS.get(language_key, LANGUAGE_HINTS["auto"])
extra = TASK_INSTRUCTIONS.get(mode, "")
token = OFFICIAL_PROMPTS.get(task, OFFICIAL_PROMPTS[TASK_OCR])
return f"{hint}{extra}{token}"
def mode_to_task(mode: str) -> str:
if mode == MODE_PRECISE:
return TASK_SPOTTING
if mode == MODE_TABLE:
return TASK_TABLE
return TASK_OCR
class PaddleOcrVlEngine:
def __init__(self) -> None:
self.model: Any = None
self.processor: Any = None
self.device: Any = None
self._lock = threading.Lock()
def load(self) -> None:
if self.model is not None:
return
from ocr_studio.config import MODEL_ID, MODEL_REVISION
import torch
from transformers import AutoConfig, AutoModelForImageTextToText, AutoProcessor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
config = AutoConfig.from_pretrained(MODEL_ID, revision=MODEL_REVISION)
if not hasattr(config, "text_config") and hasattr(config, "get_text_config"):
config.text_config = config.get_text_config()
if getattr(config, "tie_word_embeddings", None):
config.tie_word_embeddings = False
text_config = getattr(config, "text_config", None)
if text_config is not None and getattr(text_config, "tie_word_embeddings", None):
text_config.tie_word_embeddings = False
processor = AutoProcessor.from_pretrained(
MODEL_ID,
revision=MODEL_REVISION,
trust_remote_code=False,
)
model = AutoModelForImageTextToText.from_pretrained(
MODEL_ID,
config=config,
revision=MODEL_REVISION,
torch_dtype=dtype,
trust_remote_code=False,
low_cpu_mem_usage=True,
)
model = model.to(device).eval()
self.model = model
self.processor = processor
self.device = device
def _prepare_image(self, image: Image.Image, task: str) -> Image.Image:
prepared = image.convert("RGB")
if (
task == TASK_SPOTTING
and prepared.width < SPOTTING_UPSCALE_THRESHOLD
and prepared.height < SPOTTING_UPSCALE_THRESHOLD
):
prepared = prepared.resize(
(prepared.width * 2, prepared.height * 2),
Image.Resampling.LANCZOS,
)
return prepared
def _generate(self, image: Image.Image, prompt: str, task: str) -> str:
self.load()
import torch
work_image = self._prepare_image(image, task)
max_pixels = SPOTTING_MAX_PIXELS if task == TASK_SPOTTING else OCR_MAX_PIXELS
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": work_image},
{"type": "text", "text": prompt},
],
}
]
image_processor = self.processor.image_processor
min_pixels = getattr(image_processor, "min_pixels", None)
if min_pixels is None:
size_cfg = getattr(image_processor, "size", {}) or {}
min_pixels = size_cfg.get("shortest_edge") or size_cfg.get("min_pixels") or (16 * 28 * 28)
inputs = self.processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
images_kwargs={
"size": {
"shortest_edge": int(min_pixels),
"longest_edge": max_pixels,
}
},
)
inputs = inputs.to(self.model.device)
with self._lock, torch.inference_mode():
generated = self.model.generate(
**inputs,
max_new_tokens=MAX_NEW_TOKENS,
do_sample=False,
)
prompt_len = inputs["input_ids"].shape[-1]
decoded = self.processor.decode(
generated[0][prompt_len:],
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
).strip()
return decoded
def _recognize_image(
self,
image: Image.Image,
mode: str,
language_key: str,
task: str,
use_tiles: bool,
) -> PageInference:
prompt = build_prompt(mode, language_key, task)
tiled = should_tile(image, use_tiles) and task != TASK_TABLE
if not tiled:
raw = self._generate(image, prompt, task)
spans = parse_spans(raw, image.width, image.height)
display = spans_to_text(spans, strip_special_tokens(raw)).strip()
return PageInference(raw_text=raw, display_text=display, spans=spans, task=task, tile_count=1)
texts: list[str] = []
spans: list[TextSpan] = []
tile_count = 0
for tile, origin_x, origin_y in iter_tiles(image):
tile_count += 1
raw = self._generate(tile, prompt, task)
tile_spans = offset_spans(parse_spans(raw, tile.width, tile.height), origin_x, origin_y)
spans.extend(tile_spans)
texts.append(spans_to_text(tile_spans, strip_special_tokens(raw)).strip())
spans = merge_tile_spans(spans)
display = spans_to_text(spans, join_tile_text(texts)).strip()
return PageInference(
raw_text="\n".join(texts),
display_text=display,
spans=spans,
task=task,
tile_count=max(1, tile_count),
)
def recognize_pages(
self,
pages: list[Image.Image],
language_key: str,
mode: str,
high_accuracy: bool,
) -> BatchInference:
self.load()
results: list[PageInference] = []
compare = mode == MODE_COMPARE
primary_task = TASK_SPOTTING if mode in {MODE_PRECISE, MODE_COMPARE} else mode_to_task(mode)
use_tiles = high_accuracy and primary_task != TASK_TABLE
for page in pages:
primary = self._recognize_image(page, mode, language_key, primary_task, use_tiles)
if compare:
document = self._recognize_image(page, MODE_DOCUMENT, language_key, TASK_OCR, use_tiles)
primary.alt_text = document.display_text
if not primary.display_text:
primary.display_text = document.display_text
results.append(primary)
return BatchInference(pages=results, compare=compare)
def recognize(self, image: Image.Image, mode: str) -> PageInference:
task = mode_to_task(mode)
return self._recognize_image(image, mode, "auto", task, False)
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