| """字符提取（Parsing）任务。""" |
|
|
| from __future__ import annotations |
|
|
| import re |
|
|
| from ._text import clean_value, extract_by_prefix, strip_code_fence, strip_thinking |
|
|
| PROMPT = """你将被提供一张包含汉字字符的图片。请仔细观察图片，并将图片中的所有汉字字符以正确的阅读顺序提取出来。 |
| |
| 要求： |
| - 严格按图片中文字的阅读顺序输出 |
| - 仅输出识别到的字符本身，不要输出任何解释、标点补充或分析 |
| |
| 输出格式（必须严格遵守）： |
| 提取文本：<识别到的汉字字符> |
| """ |
|
|
| _OTHER_PREFIX_RE = re.compile(r"(?:字体分类|字体|分类)\s*[:：]") |
| _PREAMBLE_LINE_RE = re.compile( |
| r"^(?:好的|好|没问题|当然|根据(?:图片|图像)|这是|以下是|图片中(?:的)?(?:内容|文字|字符)?(?:为|是|如下)?|无法(?:识别|看清)|抱歉|对不起)" |
| ) |
|
|
|
|
| def _fallback_full_answer(text: str) -> str: |
| """模型不按格式输出时，把整段答案当作字符提取结果，但清理"开场白"与"其它任务前缀"。""" |
| if not text: |
| return "" |
| raw = strip_code_fence(text.strip()) |
| if not raw: |
| return "" |
|
|
| cleaned: list[str] = [] |
| for ln in raw.splitlines(): |
| s = ln.strip().strip("`").strip() |
| if not s: |
| continue |
| if _OTHER_PREFIX_RE.match(s): |
| continue |
| if _PREAMBLE_LINE_RE.match(s): |
| continue |
| m = _OTHER_PREFIX_RE.search(s) |
| if m: |
| s = s[: m.start()].rstrip() |
| if not s: |
| continue |
| cleaned.append(s) |
|
|
| if not cleaned: |
| return "" |
| candidate = "\n".join(cleaned).strip() |
| candidate = clean_value(candidate) or candidate |
| if len(candidate) < 2: |
| return "" |
| return candidate |
|
|
|
|
| def extract(answer: str) -> tuple[bool, dict[str, str | bool]]: |
| text = strip_thinking(answer) |
| extracted = extract_by_prefix( |
| text, |
| ["提取文本", "提取结果", "识别结果", "识别文本", "文本"], |
| merge_trailing_lines=True, |
| ) |
| data: dict[str, str | bool] = {} |
| if extracted: |
| data["extracted_text"] = extracted |
| return True, data |
|
|
| fb = _fallback_full_answer(text) |
| if fb: |
| data["extracted_text"] = fb |
| data["fallback"] = True |
| return True, data |
|
|
| return False, data |
|
|
