Omarrran/koshur-ocrv6

Koshur OCRv6 — Kashmiri OCR Recognition Model

Koshur OCRv6 is a Kashmiri optical character recognition (OCR) text-recognition model trained with the PaddleOCR PP-OCRv6 small recognition stack. It is intended for recognizing Kashmiri text from cropped text-line images, including Perso-Arabic/Nastaliq-style Kashmiri text.

This repository contains the trained model checkpoints, exact source code/configuration used for training, training logs, metrics, and reproducibility artifacts. The training dataset itself is intentionally not included in this model repository.

Repository status

• Training status: completed successfully
• PBS/HPC exit status: 0
• Training runtime: 16h 34m 27s
• Final epoch: 200 / 200
• Final global step: 532,800
• Recommended checkpoint: checkpoints/best_accuracy.pdparams
• Best epoch: 191

What is included

• checkpoints/
  • best_accuracy.pdparams — best validation checkpoint by norm_edit_dis; recommended for inference/evaluation.
  • latest.pdparams, latest.pdopt, latest.states — latest/final training state.
  • iter_epoch_200.* — final epoch checkpoint.
  • iter_epoch_191.* — best epoch checkpoint, when available.
  • all-epoch-checkpoints/ — full epoch checkpoint archive is being uploaded separately when available.
• artifacts/
  • train.log — full PaddleOCR training log.
  • metrics_summary.json — parsed training/evaluation summary.
  • eval_history.jsonl — validation history extracted from logs.
  • train_loss_tail.jsonl — tail of parsed step-level training loss records.
  • assets/ — pretrained/base artifacts used for the run where applicable.
• source/
  • koshur-ocrv6/ — project code, scripts, configs, tests, and documentation used for this run.
  • PaddleOCR/ — PaddleOCR source snapshot used by the training command.

What is not included

The image dataset is not uploaded here. No train/validation/test images or label-list files are included in this model repo.

Dataset provenance and split counts are documented below for reproducibility.

Dataset

Source dataset: Omarrran/Koshur_Pixel

The dataset was prepared for OCR recognition training as cropped text-image samples with labels. It was exported into PaddleOCR recognition-list format on Pragya HPC, then split into train/validation/test partitions.

Dataset split counts used in this training run:

• Training samples: 330,078
• Validation samples: 10,388
• Test samples: 6,995
• Total samples: 347,461
• Character dictionary size: 124 entries

Dataset exclusion policy:

• The dataset is excluded from this repository to keep the model release separate from data distribution.
• This repo publishes the trained OCR model, code, configs, logs, metrics, and model artifacts only.

Architecture

This run uses PaddleOCR's PP-OCRv6 small recognition architecture configured for Kashmiri OCR recognition.

High-level structure:

• OCR task: cropped text-line recognition / image-to-text
• Framework: PaddleOCR / PaddlePaddle
• Recognition stack: PP-OCRv6 small recognition model
• Losses observed in training logs:
  • CTC loss (CTCLoss)
  • NRTR loss (NRTRLoss)
  • Combined training loss (loss)
• Main validation indicator: norm_edit_dis
• Secondary metric: exact-match acc
• Max text length: 160
• Character dictionary: source/koshur-ocrv6/artifacts/charset/koshur_dict.txt

The training was initialized from:

artifacts/assets/pretrained/PP-OCRv6_small_rec_pretrained.pdparams

Methodology

Training pipeline:

1. Build/verify Kashmiri character dictionary.
2. Export OCR recognition examples into PaddleOCR-compatible recognition data.
3. Train PP-OCRv6 small recognition model for 200 epochs.
4. Evaluate periodically on the validation split using normalized edit distance and exact-match accuracy.
5. Save epoch checkpoints and a best validation checkpoint.
6. Preserve source code, configs, training logs, and checkpoints for reproducibility.

Training configuration highlights:

• Epochs: 200
• Global steps: 532,800
• GPUs: 4 × NVIDIA A100-SXM4-80GB
• Batch size per GPU/card: 64
• Eval batch step: [0,1000]
• Save epoch step: 1
• Max text length: 160
• Mixed precision flag in best metric: is_float16=False

The exact config used for training is included under:

source/koshur-ocrv6/configs/rec/koshur_ppocrv6_small_rec.yml

Results

Primary validation metric: norm_edit_dis (normalized edit-distance similarity; higher is better).

Best validation result:

• Best epoch: 191
• Best norm_edit_dis: 0.42845766519166395
• Best exact-match acc: 0.007893723519547821
• Validation FPS at best checkpoint: 1565.0712993756804

Final recorded validation result:

• Final eval index: 532
• Final norm_edit_dis: 0.42539299199315106
• Final exact-match acc: 0.006738544467906677
• Final validation FPS: 1567.270772256869

Final training step:

• Epoch: 200 / 200
• Global step: 532,800
• Learning rate: 0.000001
• Batch exact-match acc: 0.015625
• Batch norm_edit_dis: 0.385448
• CTC loss: 1.182781
• NRTR loss: 1.165309
• Combined loss: 2.351904
• Throughput: 278.53996 samples/sec

Full log and parsed histories:

• Full training log: artifacts/train.log
• Parsed summary: artifacts/metrics_summary.json
• Eval history: artifacts/eval_history.jsonl
• Training-loss tail: artifacts/train_loss_tail.jsonl

Loading and using the model

This is a PaddleOCR/PaddlePaddle checkpoint release, not a standard Hugging Face Transformers model. Use the included PaddleOCR source snapshot and config.

Example setup:

git clone https://huggingface.co/Omarrran/koshur-ocrv6
cd koshur-ocrv6
python -m venv .venv
source .venv/bin/activate
pip install -r source/koshur-ocrv6/requirements.txt

Example inference/evaluation pattern:

cd source/PaddleOCR
python tools/infer_rec.py \
  -c ../koshur-ocrv6/configs/rec/koshur_ppocrv6_small_rec.yml \
  -o Global.pretrained_model=../../checkpoints/best_accuracy \
     Global.character_dict_path=../koshur-ocrv6/artifacts/charset/koshur_dict.txt \
     Global.infer_img=/path/to/cropped_text_line_image.png

For batch evaluation, adapt:

source/koshur-ocrv6/scripts/06_eval_rec.py

Recommended checkpoint for inference/evaluation:

checkpoints/best_accuracy.pdparams

If PaddleOCR expects the checkpoint prefix rather than the full .pdparams filename, pass:

checkpoints/best_accuracy

Reproducing training

The original training command used Paddle distributed launch on Pragya HPC with 4 GPUs. The essential training arguments were:

python -m paddle.distributed.launch --devices <4 GPUs> tools/train.py \
  -c source/koshur-ocrv6/configs/rec/koshur_ppocrv6_small_rec.yml \
  -o Global.epoch_num=200 \
     Global.save_model_dir=output/koshur_ppocrv6_small_rec_full \
     Global.use_gpu=true \
     Global.distributed=true \
     Global.character_dict_path=source/koshur-ocrv6/artifacts/charset/koshur_dict.txt \
     Global.pretrained_model=artifacts/assets/pretrained/PP-OCRv6_small_rec_pretrained.pdparams \
     Global.eval_batch_step=[0,1000] \
     Global.save_epoch_step=1 \
     Global.max_text_length=160 \
     Metric.main_indicator=norm_edit_dis \
     Metric.ignore_space=false \
     Train.loader.batch_size_per_card=64 \
     Train.loader.num_workers=8 \
     Eval.loader.batch_size_per_card=64 \
     Eval.loader.num_workers=4

Dataset paths are intentionally omitted here because the dataset is not included in this model repo. Reproduction requires preparing the Omarrran/Koshur_Pixel dataset into PaddleOCR recognition-list format.

Limitations

• Exact-match accuracy is low because full-string OCR exact match is strict, especially for long Kashmiri text strings and orthographic variation.
• norm_edit_dis is the primary metric used for checkpoint selection.
• This checkpoint should be evaluated on downstream page/line crops before deployment.
• The model card reports validation metrics extracted from the training log; separate held-out test-set evaluation should be added before making benchmark or SOTA claims.
• The release is PaddleOCR-format, not Transformers-format; use the included PaddleOCR code/configs.

Citation / attribution

If you use this model, please cite or acknowledge:

• The Koshur OCRv6 training run and model release: Omarrran/koshur-ocrv6
• Dataset provenance: Omarrran/Koshur_Pixel
• PaddleOCR / PP-OCRv6 framework

License and data notes

No dataset files are redistributed in this repository. Users are responsible for checking the dataset license/provenance and downstream deployment constraints before using the model commercially or publicly.