Gemini 3 OCR - Quick Findings

Tensorlake Integration vs Direct Gemini 3

Gemini 3-Pro brings strong OCR capabilities, but its raw output still needs significant structuring before it’s usable for downstream document workflows. While integrating Gemini 3 into our Document AI pipeline, I captured a few quick observations comparing direct Gemini 3 usage vs. running it through Tensorlake’s unified extraction layer.

PDF Handling

Gemini 3 accepts PDFs directly, but does not handle page slicing.
If you want to parse only a subset of pages, the control is limited, you have to manually split the PDF and stitch results back together.

Tensorlake supports precise page slicing out-of-the-box:

1[.code-block-title]Code[.code-block-title]parse_id = doc_ai.read(
2  file_url=file_url,
3  page_range="1-3",            # Parse only pages 1–3
4  parsing_options=parsing_options,
5)

Result: Users can extract specific pages or ranges without processing the entire document.

Output Structure

Gemini 3 can generate HTML, but the structure is not well organized for downstream use:

• sections are not clearly separated
• layout elements aren’t grouped
• users must manually reorganize the structure

Let’s look at this example which is the top portion of an invoice pdf

Output from Gemini-3 generated html

1[.code-block-title]Code[.code-block-title]<!-- Page 1 -->
2<div class="page-container">
3  <div class="header">
4    <div class="company-info">
5      <h1>ARK GLOSS CLOTHING</h1>
6      <p>123 SAN SEBASTIAN ST.</p>
7      <p>LOS ANGELES, CA 90015 (US)</p>
8      <p>(123) 555-1234</p>
9      <p>info@arkglossclothing.com</p>
10      <p style="margin-top: 10px;">Sales Rep. :</p>
11    </div>
12
13    <div class="invoice-title">
14      <h1>I N V O I C E</h1>
15      <h2>INV-20212</h2>
16
17      <div class="invoice-details">
18        <table>
19          <tr><td>INVOICE DATE</td><td>01/23/2024</td></tr>
20          <tr><td>CUSTOMER TYPE</td><td>STORE</td></tr>
21          <tr><td>PO NUMBER</td><td></td></tr>
22          <tr><td>SHIP DATE</td><td>01/26/2024</td></tr>
23        </table>
24      </div>
25    </div>
26  </div>
27</div>
28...

Output as Tensorlake’s unified JSON (Gemini-3 plugged in):

1[.code-block-title]Code[.code-block-title]{
2  "page_number": 1,
3  "page_fragments": [
4    {
5      "fragment_type": "title",
6      "content": {
7        "content": "INVOICE"
8      },
9      "reading_order": 1
10    },
11    {
12      "fragment_type": "text",
13      "content": {
14        "content": "INV-20212"
15      },
16      "reading_order": 2
17    },
18    {
19      "fragment_type": "text",
20      "content": {
21        "content": "ARK GLOSS CLOTHING\n\n123 SAN SEBASTIAN ST.\nLOS ANGELES, CA 90015 (US)\n(123) 555-1234\ninfo@arkglossclothing.com"
22      },
23      "reading_order": 3
24    },
25    {
26      "fragment_type": "table",
27      "content": {
28        "content": "INVOICE DATE01/23/2024CUSTOMER TYPESTOREPO NUMBERSHIP DATE01/26/2024",
29        "html": "<table><tbody><tr><td>INVOICE DATE</td><td>01/23/2024</td></tr><tr><td>CUSTOMER TYPE</td><td>STORE</td></tr><tr><td>PO NUMBER</td><td></td></tr><tr><td>SHIP DATE</td><td>01/26/2024</td></tr></tbody></table>",
30        "markdown": "| INVOICE DATE | 01/23/2024 |\n| CUSTOMER TYPE | STORE |\n| PO NUMBER |  |\n| SHIP DATE | 01/26/2024 |"
31      },
32      "reading_order": 4
33    }
34  ]
35}
36...

How Tensorlake Differs

Tensorlake’s integration produces clean, well-organized structured output, including:

• clear layout groups
• well-defined document sections
• table structures represented cleanly in both html and markdown
• consistent fragment types that work across all OCR/VLM backends

Result: Developers receive a clean, predictable document structure without custom parsing or prompt iteration.

Advanced Usage vs. Simple Usage

Advanced Gemini users can approximate a similar level of structure with multiple prompt iterations and custom post-processing.With Tensorlake, users get a clean, structured result with a single API call.

What's Next

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