feat(detector): recurse into Form XObjects for image detection and classify vector-text pages
Images nested inside Form XObjects (e.g. HuttoISDWorkPerks.pdf) were invisible to the detector. Rewrote analyze_page_images to recursively follow Form XObjects. Also added pages_with_vector_text tracking so PDFs with vector-outlined text (e.g. qsop_21-0055.pdf) are correctly classified as IMAGE-BASED with OCR recommended. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Abimael Martell
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
c1357e79ea
commit
ea4709517e
+98
-34
@@ -186,6 +186,7 @@ pub(crate) fn detect_from_document(
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let mut pages_with_text = 0u32;
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let mut pages_with_images = 0u32;
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let mut pages_with_template_images = 0u32;
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let mut pages_with_vector_text = 0u32;
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let mut total_text_ops = 0u32;
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// Cache Phase 1 results to avoid re-analyzing sampled pages in Phase 2
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let mut analysis_cache: HashMap<u32, PageAnalysis> = HashMap::new();
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@@ -195,6 +196,13 @@ pub(crate) fn detect_from_document(
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if let Some(&page_id) = pages.get(page_num) {
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let analysis = analyze_page_content(doc, page_id);
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pages_actually_sampled += 1;
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log::debug!(
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"page {}: text_ops={} images={} image_count={} template={} unique_chars={} path_ops={} vector_text={} image_area={}",
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page_num, analysis.text_operator_count, analysis.has_images,
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analysis.image_count, analysis.has_template_image,
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analysis.unique_text_chars, analysis.path_op_count, analysis.has_vector_text,
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analysis.total_image_area
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);
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let is_image_dominated = analysis.image_count > 10
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&& analysis.image_count > analysis.text_operator_count * 3;
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let effective_min_ops = if analysis.has_images || analysis.image_count > 0 {
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@@ -215,6 +223,9 @@ pub(crate) fn detect_from_document(
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if analysis.has_template_image {
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pages_with_template_images += 1;
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}
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if analysis.has_vector_text {
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pages_with_vector_text += 1;
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}
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total_text_ops += analysis.text_operator_count;
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analysis_cache.insert(*page_num, analysis.clone());
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@@ -261,14 +272,15 @@ pub(crate) fn detect_from_document(
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} else if text_ratio >= config.text_page_ratio_threshold {
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ocr_recommended = false;
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(PdfType::TextBased, text_ratio)
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} else if pages_with_text == 0 && pages_with_images > 0 {
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} else if pages_with_text == 0 && (pages_with_images > 0 || pages_with_vector_text > 0) {
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// No extractable text but has images or vector-outlined text
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ocr_recommended = true;
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if total_text_ops == 0 {
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if total_text_ops == 0 && pages_with_vector_text == 0 {
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(PdfType::Scanned, 0.95)
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} else {
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(PdfType::ImageBased, 0.8)
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}
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} else if pages_with_text > 0 && pages_with_images > 0 {
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} else if pages_with_text > 0 && (pages_with_images > 0 || pages_with_vector_text > 0) {
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ocr_recommended = true;
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(PdfType::Mixed, 0.7)
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} else if total_text_ops == 0 {
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@@ -767,6 +779,7 @@ fn analyze_page_images(doc: &Document, page_id: ObjectId) -> (bool, u64, bool) {
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let mut has_images = false;
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let mut total_area: u64 = 0;
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let mut has_template_image = false;
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let mut visited: HashSet<ObjectId> = HashSet::new();
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if let Ok(page_dict) = doc.get_dictionary(page_id) {
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let resources = match page_dict.get(b"Resources") {
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@@ -776,60 +789,111 @@ fn analyze_page_images(doc: &Document, page_id: ObjectId) -> (bool, u64, bool) {
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};
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if let Some(resources) = resources {
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if let Ok(xobject) = resources.get(b"XObject") {
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collect_images_from_resources(
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doc,
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resources,
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&mut has_images,
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&mut total_area,
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&mut has_template_image,
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TEMPLATE_IMAGE_THRESHOLD,
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&mut visited,
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);
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}
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}
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(has_images, total_area, has_template_image)
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}
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/// Recursively collect image dimensions from XObject resources,
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/// including images nested inside Form XObjects.
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fn collect_images_from_resources(
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doc: &Document,
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resources: &lopdf::Dictionary,
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has_images: &mut bool,
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total_area: &mut u64,
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has_template_image: &mut bool,
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threshold: u64,
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visited: &mut HashSet<ObjectId>,
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) {
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let xobject = match resources.get(b"XObject") {
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Ok(obj) => obj,
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_ => return,
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};
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let xobject_dict = match xobject {
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Object::Reference(id) => doc.get_dictionary(*id).ok(),
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Object::Dictionary(dict) => Some(dict),
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_ => None,
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};
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let Some(xobject_dict) = xobject_dict else {
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return;
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};
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if let Some(xobject_dict) = xobject_dict {
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for (_, value) in xobject_dict.iter() {
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if let Ok(xobj_ref) = value.as_reference() {
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if let Ok(xobj) = doc.get_object(xobj_ref) {
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if let Ok(stream) = xobj.as_stream() {
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// Check if it's an Image subtype
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if let Ok(subtype) = stream.dict.get(b"Subtype") {
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if let Ok(name) = subtype.as_name() {
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if name == b"Image" {
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has_images = true;
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let xobj_ref = match value.as_reference() {
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Ok(r) => r,
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_ => continue,
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};
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if !visited.insert(xobj_ref) {
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continue;
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}
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let xobj = match doc.get_object(xobj_ref) {
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Ok(o) => o,
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_ => continue,
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};
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let stream = match xobj.as_stream() {
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Ok(s) => s,
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_ => continue,
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};
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let subtype = match stream.dict.get(b"Subtype") {
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Ok(s) => s,
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_ => continue,
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};
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let name = match subtype.as_name() {
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Ok(n) => n,
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_ => continue,
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};
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// Get image dimensions
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if name == b"Image" {
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*has_images = true;
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let width = stream
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.dict
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.get(b"Width")
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.ok()
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.and_then(|w| w.as_i64().ok())
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.unwrap_or(0)
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as u64;
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.unwrap_or(0) as u64;
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let height = stream
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.dict
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.get(b"Height")
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.ok()
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.and_then(|h| h.as_i64().ok())
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.unwrap_or(0)
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as u64;
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.unwrap_or(0) as u64;
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let area = width * height;
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total_area += area;
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// Check if this is a large template image
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if area >= TEMPLATE_IMAGE_THRESHOLD {
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has_template_image = true;
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*total_area += area;
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if area >= threshold {
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*has_template_image = true;
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}
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} else if name == b"Form" {
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// Recurse into Form XObject's own Resources
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if let Ok(form_resources) = stream.dict.get(b"Resources") {
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let form_res_dict = match form_resources {
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Object::Reference(id) => doc.get_dictionary(*id).ok(),
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Object::Dictionary(dict) => Some(dict),
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_ => None,
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};
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if let Some(form_res) = form_res_dict {
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collect_images_from_resources(
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doc,
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form_res,
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has_images,
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total_area,
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has_template_image,
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threshold,
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visited,
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);
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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(has_images, total_area, has_template_image)
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}
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/// Get document title from Info dictionary
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