---
title: Azerbaijani OCR — CRNN + CTC
emoji: 📜
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: 6.28.0
app_file: app.py
short_description: Read Azerbaijani text lines and whole page scans
python_version: "3.12"
startup_duration_timeout: 1h
---

# Azerbaijani OCR — CRNN + CTC

A Gradio demo for [`LocalDoc/azerbaijani-ocr-crnn`](https://huggingface.co/LocalDoc/azerbaijani-ocr-crnn),
an 8.5M-parameter CRNN + CTC recogniser for Azerbaijani print in **Latin and
Cyrillic** script (CER 0.0242 / WER 0.0565 on the authors' benchmark).

Two modes:

- **Line** — the recogniser alone on a cropped line of text. This is the model's
  native input.
- **Page** — [PP-OCRv5](https://huggingface.co/PaddlePaddle/PP-OCRv5_server_det)
  detection finds the line boxes, the recogniser reads each crop. Reading order
  assumes a single column; for two-column pages, read the left half and then the
  right half using the *Column layout* option.

`modeling.py` and the detection / reading-order logic are vendored verbatim or
adapted from the model repo (CC-BY-4.0); the weights (`model.pt`) are downloaded
from the Hub at startup.

## Limitations

From the model card, and worth knowing before you judge an output:

- **`a` ↔ `ə`.** Training labels came from OCR text layers rather than human
  transcription, so the model inherited that engine's systematic errors. The most
  common residual mistake is printing `a` where the page shows `ə`.
- **Resolution floor.** Source lines run about 20 px high; at that scale the
  Cyrillic vertical-stroke family (`и/н/п/м`) is genuinely ambiguous.
- **Greedy CTC decoding** — no language model.
- The Cyrillic benchmark figure rests on a small number of books and is
  optimistic.
- Very large pages (roughly 30+ detected lines) can exhaust the shared
  ZeroGPU memory pool while batching. The recogniser halves its batch and
  retries automatically, so those pages still finish, but a very dense page
  may take noticeably longer or fail on a busy worker — split it if that
  happens.

## Example assets

Line and page examples are real crops from
[`LocalDoc/azerbaijani-ocr-benchmark`](https://huggingface.co/datasets/LocalDoc/azerbaijani-ocr-benchmark)
(CC-BY-4.0, © LocalDoc), the authors' own evaluation set. The page examples stack
consecutive benchmark lines into a full-page layout.