| ---
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| license: cc-by-nc-4.0
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| language:
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| - tr
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| library_name: keras
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| pipeline_tag: image-to-text
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| tags:
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| - ocr
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| - text-recognition
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| - turkish
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| - tinyml
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| - tflite
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| - int8
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| - crnn
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| - ctc
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| - microcontroller
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| ---
|
|
|
| # erdem-ocr: Turkish line OCR for microcontrollers
|
|
|
| > **Newer version:** [inviolus/InviolusOCR](https://huggingface.co/inviolus/InviolusOCR) (2026-10-31) is smaller and its int8 file reads 78% of held-out lines exactly instead of 33%.
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|
|
| A compact CNN + BiLSTM + CTC recognizer for **one cropped line of printed
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| Turkish text**, built to eventually run on a microcontroller-class device.
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| It ships as a float Keras model and a **full-int8 TensorFlow Lite** model
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| (~650 KB).
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|
|
| > **Türkçe özet:** Mikrodenetleyiciler için tasarlanmış, tek satır basılı
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| > Türkçe metni okuyan küçük bir OCR modeli (yaklaşık 631 bin parametre,
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| > int8 TFLite dosyası yaklaşık 650 KB). Ticari olmayan kullanım içindir
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| > (CC BY-NC 4.0).
|
|
|
| ## Files
|
|
|
| | File | What it is |
|
| |---|---|
|
| | `erdem_ocr.keras` | Float recognition model, input `(batch, 32, 160, 1)`, output `(batch, 40, 90)` softmax |
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| | `erdem_ocr_int8.tflite` | Full-int8 export, static batch 1, input `[1, 32, 160, 1]` int8, output `[1, 40, 90]` int8 |
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| | `alphabet.json` | Character set and blank index used to decode the output |
|
|
|
| ## Model
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|
|
| - **Input:** one horizontal text line, grayscale, resized to 160×32
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| (width×height), scaled to `[-1, 1]` (`pixel / 127.5 - 1`).
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| - **Architecture:** 4 conv + batch-norm + pool blocks, 2 × bidirectional
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| LSTM (96 units), dense softmax. 631,226 parameters.
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| - **Output:** 40 timesteps × 90 classes. Class `0` is padding (never
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| predicted), classes `1..88` are the characters of `alphabet.json` in order,
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| class `89` is the CTC blank.
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| - **Character set:** digits, ASCII letters, `ç ğ ı İ ö ş ü Ç Ğ Ö Ş Ü`, space
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| and `.,:;!?-_/()'%`. Up to 24 characters per line.
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| - **Decoding:** greedy CTC (argmax per step, collapse repeats, drop blanks).
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| - **int8 quantization:** input scale `0.00784`, zero point `-1`; output scale
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| `0.00390625`, zero point `-128`.
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| - **Not included:** text detection. The model expects a line that has
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| already been cropped.
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|
|
| ## Usage
|
|
|
| ```python
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| import json
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| import numpy as np
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| import tensorflow as tf
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|
|
| alphabet = json.load(open("alphabet.json", encoding="utf-8"))["alphabet"]
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| BLANK = len(alphabet) + 1
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|
|
| def preprocess(path):
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| image = tf.io.decode_image(tf.io.read_file(path), channels=1, expand_animations=False)
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| image = tf.image.resize(image, [32, 160])
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| return (tf.cast(image, tf.float32) / 127.5 - 1.0).numpy()
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|
|
| def decode(ids):
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| text, previous = [], None
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| for i in ids:
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| if i != previous and 0 < i < BLANK:
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| text.append(alphabet[i - 1])
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| previous = i
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| return "".join(text)
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|
|
| model = tf.keras.models.load_model("erdem_ocr.keras", compile=False)
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| probabilities = model.predict(preprocess("line.png")[np.newaxis])
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| print(decode(probabilities[0].argmax(-1)))
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| ```
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|
|
| ## Results
|
|
|
| Exact-line accuracy (whole line correct) and character error rate (CER) of
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| the float model, on data it was not trained on:
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|
|
| | Test set | Samples | Exact line | CER |
|
| |---|---:|---:|---:|
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| | TS-TR official test split (real scene text) | 2,078 | 45.33% | 24.17% |
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| | Held-out synthetic + real + Zenodo lines | 3,234 | 58.23% | 12.61% |
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| | of which Zenodo (clean printed lines) | 200 | 91.00% | 1.61% |
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| | Small real-photo test set | 66 | 19.70% | 50.21% |
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|
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| **int8 TFLite model:** on the 3,993-row held-out split it reads **10.5%** of
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| lines exactly (CER 21.7%), against 55.7% for the float model. The
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| quantization loss is large and unresolved, so treat the `.tflite` file as an
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| experimental export.
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|
|
| ## Limitations
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|
|
| - Printed text only; no handwriting, curved text or full pages.
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| - Works best on clean, high-contrast lines; real scene text is still weak.
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| - The int8 model loses most of the float model's accuracy (see above).
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| - Text detection and line cropping must be done before this model runs.
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|
|
| ## Training data
|
|
|
| - Synthetic Turkish lines rendered from a 50k-word Turkish word list in
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| several fonts, with blur, noise and perspective changes.
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| - [Turkish Scene Text Recognition (TS-TR)](https://www.kaggle.com/datasets/serdaryildiz/turkish-scene-text-recognition-dataset),
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| Serdar Yıldız, CC BY-NC 4.0.
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| - [Turkish OCR Text Image Dataset](https://zenodo.org/records/21923181),
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| Zenodo, CC BY 4.0.
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| - A small number of the author's own photographs of printed text.
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|
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| Training/validation splits are group-disjoint (no font, background or
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| source group appears in both).
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|
|
| ## License
|
|
|
| Because the training data includes TS-TR (CC BY-NC 4.0), this model is
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| released under
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| **[Creative Commons Attribution-NonCommercial 4.0](https://creativecommons.org/licenses/by-nc/4.0/)**:
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|
|
| - You may use, share and adapt the model for **non-commercial** purposes.
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| - You must credit this model and the datasets listed above.
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| - Commercial use is not permitted.
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|
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| The training code is MIT-licensed:
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| [ErdemWilkinson/InviolusOCR](https://github.com/ErdemWilkinson/InviolusOCR).
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|
|
| ## Version
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|
|
| Trained 2026-10-09. Later versions of this model score higher (65% exact on
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| TS-TR) but were trained with a dataset whose license is not stated, so they
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| are not published here.
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|
|
