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README.md

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+---
+license: apache-2.0
+tags:
+  - image-classification
+  - document-classification
+  - timm
+  - efficientnet
+library_name: timm
+pipeline_tag: image-classification
+---
+
+# LipiOCR Document Classifier
+
+A lightweight document-type classifier — the classification stage of
+**LipiOCR**, a fast document intelligence pipeline (classify → OCR →
+QR/barcode decode → normalize) built as a speed-focused complement to
+[ZuraAI-VL](https://ollama.com/bsspl/zuraai-vl-v3), a larger VLM used for
+full structured extraction.
+
+This model does one job well: given a photographed or scanned document,
+tell you what type of document it is, in milliseconds, on CPU or GPU.
+It does not read text — pair it with an OCR stage (LipiOCR uses
+[glm-ocr](https://ollama.com/bsspl/glm-ocr)) for full extraction.
+
+## Model details
+
+- **Architecture:** EfficientNet-B0 (via `timm`), ImageNet-pretrained, fine-tuned end-to-end
+- **Input:** 224×224 RGB image
+- **Output:** 1 of 42 document type labels
+- **Parameters:** ~5.3M
+
+## Categories (42)
+
+Identity: `aadhaar_card`, `cid`, `driving_license`, `nic`, `pan_card`, `passport`, `visa`, `voter_id`
+Financial: `bank_cheque`, `bank_statement`, `invoice`, `pos_payment_slip`, `purchase_order`, `quotation`, `receipt`, `telegraphic_transfer`, `payment_voucher`, `credit_card`
+Travel: `airway_bill`, `boarding_pass`, `courier_waybill`
+Certificates: `birth_certificate`, `certificate`, `marriage_certificate`, `medical_certificate`, `vaccination_certificate`
+Business/other: `business_card`, `employment_offer_letter`, `general_letter`, `income_tax_return`, `insurance_policy`, `marksheet`, `medical_prescription`, `menu_card`, `noc`, `product_catalog`, `reference_letter`, `rental_agreement`, `resume`, `salary_slip`, `utility_bill`, `vehicle_registration`
+
+## Training data
+
+- 7,227 training images across 42 categories, mostly synthetic (Faker-generated documents rendered with realistic layouts, colors, and — for the financial categories — real decodable QR/barcodes)
+- A small set of real (non-synthetic) photographed/scanned documents, oversampled 15x during training to avoid being drowned out by the synthetic majority
+- Training augmentation includes perspective warp, paper texture, directional lighting gradients, JPEG artifacts, and axis-aligned rotation — added specifically because a first version trained only on clean synthetic renders scored **0/11 on real photographed documents** despite 98%+ synthetic validation accuracy. These fixes brought real-document accuracy to roughly 90%+ on most categories (see Limitations).
+
+## Performance
+
+- **99.66%** validation accuracy on a held-out synthetic split (1,167 examples)
+- **11/12** real photographed documents correctly classified in a spot validation across categories with real examples available
+
+## Usage
+
+```python
+import json
+import torch
+import timm
+from PIL import Image
+from torchvision import transforms
+from safetensors.torch import load_file
+
+config = json.load(open("config.json"))
+model = timm.create_model(config["architecture"], pretrained=False, num_classes=config["num_classes"])
+model.load_state_dict(load_file("model.safetensors"))
+model.eval()
+
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(config["normalize_mean"], config["normalize_std"]),
+])
+
+img = Image.open("document.jpg").convert("RGB")
+# Recommended: run CLAHE contrast normalization + deskew on `img` before
+# this step for best real-world accuracy - see preprocess.py in the
+# LipiOCR repo. A plain resize works but skips that robustness step.
+x = preprocess(img).unsqueeze(0)
+
+with torch.no_grad():
+    probs = torch.softmax(model(x), dim=1)[0]
+top = torch.argmax(probs).item()
+print(config["labels"][top], probs[top].item())
+```
+
+## Limitations
+
+- **Marksheet vs. resume confusion**: both render as structured line-item
+  lists (subjects/grades vs. job history/skills), and this remains the
+  weakest confusion pair in testing.
+- **Real-world academic transcripts** (marksheets specifically) are the
+  hardest category — real institutions' transcript layouts vary far more
+  than any other document type, and the training set has limited real
+  examples for this category specifically (~40% real-world accuracy vs.
+  ~90%+ for most other categories).
+- Trained primarily on English-language, Latin-script documents.
+- No language identification or OCR capability — classification only.
+
+## Part of LipiOCR
+
+This model is one stage of a larger pipeline. The full system (OCR
+extraction, QR/barcode decoding, digit-swap correction, and field
+normalization) lives in the LipiOCR project alongside this classifier.

config.json

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+{
+  "architecture": "efficientnet_b0",
+  "framework": "timm",
+  "num_classes": 42,
+  "labels": [
+    "aadhaar_card",
+    "airway_bill",
+    "bank_cheque",
+    "bank_statement",
+    "birth_certificate",
+    "boarding_pass",
+    "business_card",
+    "certificate",
+    "cid",
+    "courier_waybill",
+    "credit_card",
+    "driving_license",
+    "employment_offer_letter",
+    "general_letter",
+    "income_tax_return",
+    "insurance_policy",
+    "invoice",
+    "marksheet",
+    "marriage_certificate",
+    "medical_certificate",
+    "medical_prescription",
+    "menu_card",
+    "nic",
+    "noc",
+    "pan_card",
+    "passport",
+    "payment_voucher",
+    "pos_payment_slip",
+    "product_catalog",
+    "purchase_order",
+    "quotation",
+    "receipt",
+    "reference_letter",
+    "rental_agreement",
+    "resume",
+    "salary_slip",
+    "telegraphic_transfer",
+    "utility_bill",
+    "vaccination_certificate",
+    "vehicle_registration",
+    "visa",
+    "voter_id"
+  ],
+  "input_size": [
+    224,
+    224
+  ],
+  "normalize_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "normalize_std": [
+    0.229,
+    0.224,
+    0.225
+  ]
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
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+size 16446552