Repair CVs that pypdf extracts one character per line

Reported as "LinkedIn is not showing though the resume has it". The LinkedIn
was never the problem.

Traced on the live application (Mohammad Raza, inbox row 2f81cebc). Its stored
resume_text is 4,555 characters over 2,278 lines, and every one of those lines
is exactly one character long. The CV really does say

    LinkedIn:linkedin.com/in/mohammad-raza-digital-marketer

but it is stored as forty separate lines, so nothing that looks for a substring
can find it. Not a link annotation, not an image, not OCR: pypdf's default mode
breaks after every glyph on PDFs whose author positioned each one separately,
which design tools do routinely.

It survived review because a model reads that text fine. The candidate was
classified, matched and scored normally. What fails, silently, is every check
that asks "does this string appear in the resume":

  - slugs_from_text finds no profile, so linkedin_slug is stored empty
  - _clean_skills drops every skill, since each must appear in the text
  - the company and education clamps drop theirs for the same reason
  - verify_matched_keywords drops every matched keyword in the ATS engine

despace_line could not help: it rebuilds glyphs padded *within* a line, and
here there is nothing left on a line to rebuild.

is_glyph_fragmented measures the giveaway — the share of non-empty lines that
are a single character — and extract_pdf_text re-extracts with pypdf's layout
mode when it trips. Layout mode is the fallback, never the default: it is
slower and pads ordinary documents with alignment whitespace, so a CV that
extracts cleanly today is untouched. The fallback is checked before it is
trusted; fragmented text still scores a candidate, empty text fails them.

Both extractors had the defect, so the helpers live in app/services/pdf.py,
which owns PDF handling and is already imported by the recruiting path.

Measured against that real CV, before and after:

    slugs_from_text        []            -> ['mohammad-raza-digital-marketer']
    profile_url_from_text  None          -> https://www.linkedin.com/in/...
    lines                  2278          -> 61
    single-char lines      2278          -> 0
    'performance' found    False         -> True
    'google ads' found     False         -> True

Existing rows keep their broken text; extraction runs at ingest. Re-running
the match on affected rows is what backfills them.

.gitignore had `tests/**` twice and `/backend/tests/**` once. Both suites are
tracked and both run in CI, so the rules were inert for existing files and did
nothing but swallow new ones — this test was invisible to `git status` until
they went. That is also why they are removed rather than negated.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

.gitignore

@@ -71,7 +71,6 @@ Utopia-ai-hr-ats-portal 1.pem
 
 # Local-only Compose overrides (never deployed)
 docker.local.env
-tests/**
 **/.env**
 
 # Paper form source documents (Annexure A/E/J) — reference material, not code.
@@ -81,8 +80,12 @@ frontend/dist/** */
 docker.local.frontend/dist/** */
 frontend/dist/index.html
 frontend/dist/index.html
-tests/**
-/backend/tests/**
+# `tests/**` and `/backend/tests/**` used to sit here. Both test suites are
+# tracked and both are run by scripts/ci-checks.sh, so the rules were inert for
+# the files that already existed and did nothing but silently swallow NEW ones:
+# a test added to either suite never showed up in `git status`, and CI ran a
+# suite that did not include it. Removed rather than negated, because there is
+# nothing under either path that should be ignored.
 frontend/dist/**
 nginx.conf
 smoke.test.mjs

app/services/pdf.py

@@ -12,6 +12,7 @@ import re
 import uuid
 from dataclasses import dataclass
 from pathlib import PurePosixPath, PureWindowsPath
+from typing import Literal
 
 from pypdf import PdfReader
 
@@ -82,6 +83,55 @@ def _normalize_text(text: str) -> str:
     return text.strip()
 
 
+def is_glyph_fragmented(text: str | None, *, min_lines: int = 20, ratio: float = 0.4) -> bool:
+    """True when pypdf emitted one character per line instead of words.
+
+    Design tools that position every glyph separately (Canva, InDesign and
+    friends) make pypdf's default mode break after each one, so a CV reading
+    "LinkedIn: linkedin.com/in/jane" arrives as thirty single-character lines.
+    A model reads that fine, which is why it hides: what breaks is every
+    substring check downstream. ``verify_matched_keywords`` drops every keyword,
+    and on the recruiting side the LinkedIn scan and the skills, company and
+    education clamps all return nothing, silently.
+
+    ``min_lines`` stops a two-line PDF or a near-empty page from tripping the
+    check on a handful of legitimately short lines.
+    """
+    lines = [ln.strip() for ln in (text or "").splitlines() if ln.strip()]
+    if len(lines) < min_lines:
+        return False
+    singles = sum(1 for ln in lines if len(ln) == 1)
+    return singles / len(lines) >= ratio
+
+
+def extract_pdf_text(reader: PdfReader) -> str:
+    """Page text from a reader, repaired when the default mode shatters it.
+
+    Default mode first: it is faster and already correct for ordinary CVs.
+    Layout mode is the fallback, never the default -- it rebuilds the page from
+    glyph coordinates, which recovers word and line structure on a fragmented
+    file but is slower and pads ordinary documents with alignment whitespace.
+    Reaching for it only when the default output is measurably broken means a
+    CV that extracts cleanly today keeps extracting exactly as it does now.
+
+    The fallback is checked before it is trusted: if layout mode comes back
+    fragmented too, or empty, the default text is kept. Fragmented text still
+    scores a candidate; empty text fails them outright.
+    """
+    default = "\n".join((page.extract_text() or "") for page in reader.pages)
+    if not is_glyph_fragmented(default):
+        return default
+    try:
+        layout = "\n".join(
+            (page.extract_text(extraction_mode="layout") or "") for page in reader.pages
+        )
+    except Exception:  # older pypdf, or a page layout mode chokes on
+        return default
+    if not layout.strip() or is_glyph_fragmented(layout):
+        return default
+    return layout
+
+
 def _truncate(text: str, max_chars: int) -> tuple[str, bool]:
     """Cut at a line boundary near the limit rather than mid-word."""
     if len(text) <= max_chars:
@@ -120,13 +170,26 @@ def extract_resume(data: bytes, filename: str, max_chars: int) -> ExtractedResum
     if not pages:
         raise InvalidPDFError("document has no pages")
 
-    page_texts: list[str] = []
-    for page in pages:
-        try:
-            raw_text = page.extract_text() or ""
-        except Exception:  # a single bad page must not sink the whole document
-            raw_text = ""
-        page_texts.append(_normalize_text(raw_text))
+    def _pages_in_mode(mode: Literal["plain", "layout"]) -> list[str]:
+        out: list[str] = []
+        for page in pages:
+            try:
+                raw = page.extract_text(extraction_mode=mode) or ""
+            except Exception:  # a single bad page must not sink the whole document
+                raw = ""
+            out.append(_normalize_text(raw))
+        return out
+
+    page_texts = _pages_in_mode("plain")
+    # The same repair extract_pdf_text performs, but page by page, because the
+    # page markers below need the split preserved. The whole document is judged
+    # together and then every page is re-extracted in one mode, so a document
+    # cannot end up half in each.
+    if is_glyph_fragmented("\n".join(page_texts)):
+        repaired = _pages_in_mode("layout")
+        joined = "\n".join(repaired)
+        if joined.strip() and not is_glyph_fragmented(joined):
+            page_texts = repaired
 
     body = "\n".join(chunk for chunk in page_texts if chunk)
     if len(body) < _MIN_USABLE_CHARS or not _ALPHANUMERIC.search(body):

backend/job/candidate/plugins.py

@@ -19,6 +19,11 @@ from pathlib import Path
 
 from app.core.config import Settings, get_settings
 from app.services.llm import OpenAIScorer
+# One definition, two extractors. The bulk-ATS engine and this recruiting path
+# both read CVs with pypdf and both broke the same way on glyph-fragmented
+# files, so the repair lives in the package that owns PDF handling and is
+# re-exported here for the callers that import it from this module.
+from app.services.pdf import extract_pdf_text, is_glyph_fragmented  # noqa: F401
 from dotenv import load_dotenv
 
 from job.candidate.decorators import despace_line, normalize_unicode

backend/job/candidate/views.py

@@ -19,6 +19,7 @@ from job.candidate.plugins import (
     contained_download_path,
     documents_from_message,
     extract_pdf_link_uris,
+    extract_pdf_text,
     get_scorer,
     get_scoring_settings,
     normalize_spaced_text,
@@ -125,8 +126,10 @@ class FileRead:
             reader = PdfReader(io.BytesIO(self.file))
             if reader.is_encrypted:
                 raise HTTPException(400, "PDF is password protected")
-            pages = [(page.extract_text() or "") for page in reader.pages]
-            text = normalize_spaced_text("\n".join(pages))
+            # extract_pdf_text, not page.extract_text() directly: some CVs come
+            # out of pypdf one character per line, which reads fine to the LLM
+            # but defeats every substring check downstream. See its docstring.
+            text = normalize_spaced_text(extract_pdf_text(reader))
             # Icon-only LinkedIn buttons never appear in extract_text(); the
             # URL is on the annotation. Append so the employment agent can
             # return linkedin_url as its own parsed key.

tests/unit/test_pdf_fragmentation.py

@@ -0,0 +1,97 @@
+"""Glyph-fragmented CVs must not silently lose every substring check.
+
+The defect these cover, found on a live application: a CV whose PDF positions
+each glyph separately came out of pypdf's default mode as one character per
+line. The text was all there, so the LLM read it and scored the candidate
+fine — but `linkedin.com/in/...` was spelled across forty lines, so the
+LinkedIn scan found nothing, and so did the skills, company and education
+clamps, which all ask whether a string appears in the resume.
+
+No real PDF and no pypdf call: a stub reader returns canned page text, which
+is the only input the function under test actually reads.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from app.services.pdf import extract_pdf_text, is_glyph_fragmented
+
+# The real shape of the failure, taken from the CV that exposed it.
+FRAGMENTED = "\n".join("LinkedIn:linkedin.com/in/mohammad-raza-digital-marketer")
+REPAIRED = (
+    "MOHAMMAD RAZA\n"
+    "Performance Marketing Specialist\n"
+    "Karachi, Pakistan | +923362837939\n"
+    "LinkedIn:linkedin.com/in/mohammad-raza-digital-marketer\n"
+)
+ORDINARY = "\n".join(f"Line {i} of an ordinary resume with real words on it." for i in range(30))
+
+
+class _Page:
+    """Minimal pypdf page: default text, and optionally a layout-mode variant."""
+
+    def __init__(self, default, layout=None, layout_raises=False):
+        self._default = default
+        self._layout = layout
+        self._layout_raises = layout_raises
+
+    def extract_text(self, extraction_mode="plain"):
+        if extraction_mode == "layout":
+            if self._layout_raises:
+                raise ValueError("layout mode unsupported")
+            return self._layout
+        return self._default
+
+
+class _Reader:
+    def __init__(self, *pages):
+        self.pages = list(pages)
+
+
+class TestIsGlyphFragmented:
+    def test_one_character_per_line_is_fragmented(self):
+        assert is_glyph_fragmented(FRAGMENTED) is True
+
+    def test_ordinary_text_is_not(self):
+        assert is_glyph_fragmented(ORDINARY) is False
+
+    def test_short_input_never_trips_it(self):
+        # A two-line PDF of initials is not evidence of a broken extractor, and
+        # treating it as such would send every tiny document through layout mode.
+        assert is_glyph_fragmented("A\nB\nC") is False
+
+    @pytest.mark.parametrize("text", ["", None])
+    def test_empty_is_not_fragmented(self, text):
+        assert is_glyph_fragmented(text) is False
+
+
+class TestExtractPdfText:
+    def test_ordinary_pdf_is_returned_untouched(self):
+        # The guarantee that matters most: a CV that extracts cleanly today must
+        # keep extracting byte-identically, never routed through layout mode.
+        reader = _Reader(_Page(ORDINARY, layout="LAYOUT SHOULD NOT BE USED"))
+        assert extract_pdf_text(reader) == ORDINARY
+
+    def test_fragmented_pdf_falls_back_to_layout(self):
+        reader = _Reader(_Page(FRAGMENTED, layout=REPAIRED))
+        out = extract_pdf_text(reader)
+        assert out == REPAIRED
+        assert "linkedin.com/in/mohammad-raza-digital-marketer" in out.lower()
+
+    def test_layout_that_is_also_fragmented_is_rejected(self):
+        reader = _Reader(_Page(FRAGMENTED, layout=FRAGMENTED))
+        assert extract_pdf_text(reader) == FRAGMENTED
+
+    def test_empty_layout_is_rejected(self):
+        # Fragmented text still scores a candidate. Empty text fails them outright.
+        reader = _Reader(_Page(FRAGMENTED, layout="   "))
+        assert extract_pdf_text(reader) == FRAGMENTED
+
+    def test_layout_mode_unsupported_falls_back_to_default(self):
+        reader = _Reader(_Page(FRAGMENTED, layout_raises=True))
+        assert extract_pdf_text(reader) == FRAGMENTED
+
+    def test_pages_are_joined(self):
+        reader = _Reader(_Page("page one text here"), _Page("page two text here"))
+        assert extract_pdf_text(reader) == "page one text here\npage two text here"