language:
- fa
license: other
license_name: bina-0.4-rizeh
license_link: >-
https://huggingface.co/Reza2kn/Bina-0.4-Rizeh/blob/51b8c314b0552b1884c1f0e28016cb12a143f637/README.md
base_model: Reza2kn/Bina-0.4-Rizeh
base_model_relation: quantized
pipeline_tag: image-to-text
tags:
- bina
- ocr
- int4
- coreml
Bina0.4 Rizeh — CoreML-INT4
Quantized CTC reader components and experimental line-detector conversions of the final release, pinned at 51b8c314b0552b1884c1f0e28016cb12a143f637. All five readers are included: body95k, short labels, printed, numeric/math and degraded scans. Original pipeline code and nonconverted dependencies remain in the source repository.
These are mixed-weight4-bit artifacts. They are not an all-INT4 document pipeline. The reference Python pipeline also uses Surya layout/order, native detector paths, table machinery and small admission/ordering models. No Sherpa-ONNX OCR support is claimed.
Measured reader parity
Fresh uncompressed PyTorch2.10 CUDA baselines were generated for each published checkpoint. Each target uses the same300 frozen internal development crops (50 each: English, handwriting, mixed, newspaper, printed, synthetic). No text normalization is applied to parity. At least98% character parity is required in every bucket; exact-line agreement is reported separately in VALIDATION.json. These are development canaries, not held-out accuracy or full-page certification.
| Runtime | Reader components | Lowest bucket character parity |
|---|---|---|
| coreml-w4-block16 | 5/5 | 98.466% |
Mac timings were collected during concurrent conversion/browser work and are not controlled speed rankings. Linux CPU/CUDA results are host-specific; native Windows is untested. LiteRT was tested on Linux CPU, without an Android test device. Apple validation used an M2 Mac; iOS hardware was not tested.
Page-pipeline limitation
The public release demo matched the original text in a small ONNX page test, and exact reader-cache reuse avoided recognition calls on its repeat. A broader public handwritten-table sample exposed a failure: the pure-W4 pipeline changed the output substantially even though line canaries passed. Therefore this bundle does not pass the full-page98% transcription gate and is not a drop-in fully qualified4-bit replacement for the production pipeline. Detector mask IoU is not transcription parity.
The experimental Python adapter preserves upstream crop geometry, routing and decoding. It also partitions an upstream geometry-reread cache by actual backend/weight hash, preventing cross-model cache reuse. Its optional image-only safety route uses original FP32 readers on color-separated handwritten pages, before OCR, avoiding a trial quantized pass. That route is mixed-precision and requires separate full-pipeline qualification. It does not make the pure-W4 model pass.
Line-level use
Install requirements.txt, then run python inference.py line.png --role body. This expects one text line or an explicit line box through Recognizer.read_line; it does not detect or assemble a whole document. Persistent sessions and bounded exact-input caching are included. Widths are never squashed or truncated.
Inputs are64px-high float32 ink maps with aspect preserved, black=1/paper=0,20% vertical and30% horizontal margins measured in original line height. True widths retain masking semantics. CTC blank is0 and nonblank indices map to vocab.json. Persian visual-to-logical conversion follows the original rtl.py.
Core ML ML Program packages require iOS18/macOS15 or later. Inputs: pixels float32 [B,1,64,W], widths int32 [B]; batch1..8 and width8..8192. Use CPU_AND_GPU. W4/block16 applies to compatible large weights, retaining floating operations where required. GPU-only/ANE placement is not claimed.
Experimental detectors
Three detector roles are included under experimental-detectors/: combined main detector1000, rule-C line guard500 and the printed detector/head. Their procedural-dev heatmaps were compared against their original FP32 models. Their full geometry/table/transcription impact is unqualified. Do not replace the production detectors based on mask IoU alone.
Provenance and licensing
Quantization did not alter the source repository. The aggregate model.safetensors was verified tensor-for-tensor against the published native core checkpoints. Each component records its exact source checkpoint SHA256 in quantization_config.json. MANIFEST.json hashes the shipped artifacts. No private calibration images, benchmark fixtures, or private prediction texts are shipped.
Original recognizer weights/code remain under the source release CC BY-NC4.0 terms; Surya-derived detector/layout components retain their upstream Surya model terms. These conversions grant no additional rights. Retain the original Bina/Indra/Surya attribution. See the source release model card for details.