Gemini 3 OCR - Quick Findings
TL;DR
Gemini 3 has good OCR but is unstructured and limited. Tensorlake provides precise page slicing and well-structured JSON output with no cleanup required.
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:
1parse_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<!-- 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 <div class="invoice-title">
13 <h1>I N V O I C E</h1>
14 <h2>INV-20212</h2>
15 <div class="invoice-details">
16 <table>
17 <tr><td>INVOICE DATE</td><td>01/23/2024</td></tr>
18 <tr><td>CUSTOMER TYPE</td><td>STORE</td></tr>
19 <tr><td>PO NUMBER</td><td></td></tr>
20 <tr><td>SHIP DATE</td><td>01/26/2024</td></tr>
21 </table>
22 </div>
23 </div>
24 </div>
25....Output as Tensorlake’s unified JSON (Gemini-3 plugged in):
1{
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
22
23123 SAN SEBASTIAN ST.
24LOS ANGELES, CA 90015 (US)
25(123) 555-1234
26info@arkglossclothing.com",
27 },
28 "reading_order": 3,
29 },
30 {
31 "fragment_type": "table",
32 "content": {
33 "content": "INVOICE DATE01/23/2024CUSTOMER TYPESTOREPO NUMBERSHIP DATE01/26/2024",
34 "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>",
35 "markdown": "| INVOICE DATE | 01/23/2024 |
36| CUSTOMER TYPE | STORE |
37| PO NUMBER | |
38| SHIP DATE | 01/26/2024 |",
39 },
40 "reading_order": 4,
41 },
42...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#
Want to discuss your specific use case?
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Dr Shanshan Wang
Founding Data Scientist & Document AI Lead at Tensorlake
Founding Data Scientist & Document AI Lead who specializes in document parsing, multimodal OCR, structure extraction, and production-grade applied AI systems.