# 🔍 ASSETS NEEDED FOR FINAL REPORT: Rakīm AI (رَقِيم)

To write the final polished official paper/report, please compile and provide the following assets. These will be embedded as figures, benchmarks, and data tables to impress the national competition judges.

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## 1. Real Application Screenshots
You can capture these directly from your browser by running the local server (`python run.py` inside `backend`) and navigating to `http://localhost:8001/app`:

* **Figure 1: Pipeline Overview (Interface)**
  * *What to capture:* A widescreen screenshot of the workspace after uploading `صور/واضحة_1926.jpg` and running HTR. It should show the original manuscript with green/blue bounding boxes on the right and the clean Arabic transcription on the left.
* **Figure 2: Text Margin Separation (Matn vs. Hashiya)**
  * *What to capture:* Page `صور/حواشٍ_1960.jpg` loaded. Take two crops:
    1. Clicking **📚 الكل** (showing all bounding boxes, including marginalia).
    2. Clicking **📄 المتن** (showing only main text boxes highlighted, illustrating the classification).
* **Figure 3: Interactive Word Alternatives (Reading Candidates)**
  * *What to capture:* Click a low-confidence line (e.g. containing "المقربير"), open the line pop-up window, click **❓ بدائل**, and capture the dropdown list displaying candidate percentages ranking "المقربين" (55.8%) at the top.
* **Figure 4: AI Analysis & Contextual Explanations**
  * *What to capture:* The AI panel/modal displaying:
    1. Suggestions for page summary and title: *«قصص الخلق وأول النور»*.
    2. A list of extracted named entities (وهب بن منبه, سفيان الثوري, Ibn Abbas).
    3. The dictionary definition of terms in context (e.g., "المنطق" or "الريح العقيم").
* **Figure 5: Fuzzy Manuscript Search**
  * *What to capture:* Type "الحوت" in the search box, click Search, and capture the workspace showing the matching lines highlighted.
* **Figure 6: Manuscript Catalog Search & Content Matcher**
  * *What to capture:* The "فهرس المخطوطات" tab showing a search for "شرح فصول أبقراط", displaying the list of copies and matched metadata fingerprint terms.

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## 2. Dataset and Calligraphy Details
* **Visual Families of Hands:**
  * If available, please provide the visual cluster parameters of the **~8 manuscript families** (e.g., line count, line spacing, stroke thickness, or average character heights).
* **Source Collections:**
  * The names of the specific libraries or archives from which the training manuscript subsets were obtained (e.g., BULAC, Wellcome Collection).
* **RASAM & TariMa Scientific References:**
  * Standard bibliographic details or citations for the RASAM (Arabic manuscript dataset) and TariMa corpuses.

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## 3. Structural Graphics (Raw Images / Line Segmentations)
* **BLLA Segmentation Output:**
  * If you have training logs or visualization outputs comparing raw segmentations between `seg_best` and `logic_philosophy_v2_seg` (e.g., showcasing how the logic segmenter handles bordered margins without shattering), please provide them.
* **Over-segmentation Samples:**
  * Sample crops of Page 417 or Page 1982 showing lines broken into stray fragments, to illustrate the layout classification bottleneck in the "Limitations" section.