| """Character vocabulary for the CTC recognizer. Index 0 is the CTC blank.""" |
|
|
| import re |
| import string |
| import unicodedata |
|
|
| LATIN_EXTRA = "ÀÁÂÄÃÅÇÈÉÊËÌÍÎÏÑÒÓÔÖÕØÙÚÛÜÝßàáâäãåçèéêëìíîïñòóôöõøùúûüýÿ" |
| SYMBOLS = "€£¥₹¢°±×÷§©®™•–—‘’“”…½¼¾μ" |
| |
| |
| SUPERSCRIPT = dict(zip("0123456789+-=()ni", "⁰¹²³⁴⁵⁶⁷⁸⁹⁺⁻⁼⁽⁾ⁿⁱ")) |
| SUBSCRIPT = dict(zip("0123456789+-=()aeoxhklmnpstijruv", "₀₁₂₃₄₅₆₇₈₉₊₋₌₍₎ₐₑₒₓₕₖₗₘₙₚₛₜᵢⱼᵣᵤᵥ")) |
| GREEK = "αβγδεζηθκλνξπρστφχψωΓΔΘΛΞΠΣΦΨΩ" |
| MATH = "≤≥≠≈≡∞√∫∂∇∈∉⊂⊆∪∩∀∃→←↔⇒⇔∝∠⊥∥" |
|
|
| CHARS = (string.digits + string.ascii_letters + string.punctuation + " " + LATIN_EXTRA + SYMBOLS |
| + "".join(SUPERSCRIPT.values()) + "".join(SUBSCRIPT.values()) + GREEK + MATH) |
| MATH_CHARS = frozenset("".join(SUPERSCRIPT.values()) + "".join(SUBSCRIPT.values()) + GREEK + MATH + "μ") |
| BLANK = 0 |
| CHAR_TO_ID = {c: i + 1 for i, c in enumerate(CHARS)} |
| NUM_CLASSES = len(CHARS) + 1 |
| |
| MATH_ID = CHAR_TO_ID["⁰"] |
| EXTRACTOR_CLASSES = MATH_ID + 1 |
|
|
| |
| |
| _REPLACE = {" ": " ", "\t": " ", " ": " ", " ": " ", "‐": "-", "‑": "-", "−": "-", |
| "‚": ",", "„": "\"", "′": "'", "″": "\"", "·": ".", "⋅": ".", "▪": "•", "●": "•", |
| "µ": "μ", "ϵ": "ε", "ϕ": "φ", "ϑ": "θ", "ς": "σ", "∆": "Δ", "∑": "Σ", "∏": "Π", "∗": "*", "∕": "/", |
| "⁄": "/", "∼": "~", "⩽": "≤", "⩾": "≥", "≦": "≤", "≧": "≥", "ο": "o", "ι": "i", "υ": "u", |
| **dict(zip("ΑΒΕΖΗΙΚΜΝΟΡΤΥΧ", "ABEZHIKMNOPTYX"))} |
| _SCRIPT_SPECIAL = {"TM": "™", "tm": "™"} |
| _SCRIPT_MARKUP = re.compile(r"\$([_^])\{([^{}$]*)\}\$") |
|
|
|
|
| def scripted(chars: str, kind: str) -> str: |
| """Spell a raised ("^") or lowered ("_") run: in Unicode when every character has a form, else plainly.""" |
| table = SUPERSCRIPT if kind == "^" else SUBSCRIPT |
| if kind == "^" and chars in _SCRIPT_SPECIAL: |
| return _SCRIPT_SPECIAL[chars] |
| return "".join(table[c] for c in chars) if chars and all(c in table for c in chars) else chars |
|
|
|
|
| def latex_scripts_to_unicode(text: str) -> tuple[str, bool]: |
| """'H$_{2}$O' -> 'H₂O' (the markup PDF text extraction uses for sub/superscripts). |
| Returns (text, clean); clean is False when '$' markup this could not convert is left over.""" |
| out, n = _SCRIPT_MARKUP.subn(lambda m: scripted(m.group(2).strip(), m.group(1)), text) |
| return out, "$" not in out or out.count("$") == text.count("$") - 2 * n and "$_{" not in out and "$^{" not in out |
|
|
|
|
| _SUP_RUN = re.compile("[" + "".join(SUPERSCRIPT.values()) + "]+") |
| _SUB_RUN = re.compile("[" + "".join(SUBSCRIPT.values()) + "]+") |
| _SUP_BACK = {v: k for k, v in SUPERSCRIPT.items()} |
| _SUB_BACK = {v: k for k, v in SUBSCRIPT.items()} |
|
|
|
|
| def scripts_to_latex(text: str) -> str: |
| """'x² + H₂O' -> 'x$^{2}$ + H$_{2}$O', the usual way Markdown writes inline maths.""" |
| text = _SUP_RUN.sub(lambda m: "$^{" + "".join(_SUP_BACK[c] for c in m.group()) + "}$", text) |
| return _SUB_RUN.sub(lambda m: "$_{" + "".join(_SUB_BACK[c] for c in m.group()) + "}$", text) |
|
|
|
|
| def normalize(text: str) -> str: |
| """Map text into the vocabulary: fold known variants, strip accents only when needed, drop the rest.""" |
| out = [] |
| for ch in text: |
| ch = _REPLACE.get(ch, ch) |
| if ch in CHAR_TO_ID: |
| out.append(ch) |
| continue |
| base = unicodedata.normalize("NFKD", ch) |
| out.extend(c for c in base if c in CHAR_TO_ID) |
| return " ".join("".join(out).split()) |
|
|
|
|
| def encode(text: str) -> list[int]: |
| return [CHAR_TO_ID[c] for c in text if c in CHAR_TO_ID] |
|
|
|
|
| def ctc_greedy_decode(probs, ids=None): |
| """probs: (T, C) numpy array of per-step probabilities. Returns (text, per-char confidences).""" |
| best = probs.argmax(-1) if ids is None else ids |
| text, confs, prev = [], [], BLANK |
| for t, k in enumerate(best): |
| k = int(k) |
| if k != BLANK and k != prev: |
| text.append(CHARS[k - 1]) |
| confs.append(float(probs[t, k])) |
| prev = k |
| return "".join(text), confs |
|
|
