doctr.cpp — ggml port of doCTR

C++ port of Mindee doCTR built directly on ggml. Two stages:

Detection — db_mobilenet_v3_large backbone + DBNet head → probability map.
Recognition — crnn_mobilenet_v3_small backbone + BiLSTM + CTC head → per-crop logits.

The post-processing (DBNet contour → bbox, CTC greedy decode) stays in TypeScript — both are trivial and runtime-portable. This C++ library runs only the forward pass; the JS caller orchestrates det → crop → rec → decode.

Status

Phase 1 (current): FFI surface scaffolded; weight conversion script authored; build glue authored. GGUF weight files are not yet built. The TS binding throws a clear error until vision/doctr-det.gguf and vision/doctr-rec.gguf are present on disk.

Build (when implemented)

bash

cd plugins/plugin-vision/native/doctr.cpp
cmake -B build -S . -DGGML_METAL=ON   # macOS arm64
cmake --build build --config Release

Produces a single shared library libdoctr.dylib / .so / .dll consumed via bun:ffi from plugin-vision/src/native/doctr-ffi.ts.

Convert weights (when implemented)

bash

python scripts/convert.py \
  --variant db_mobilenet_v3_large \
  --out vision/doctr-det.gguf

python scripts/convert.py \
  --variant crnn_mobilenet_v3_small \
  --out vision/doctr-rec.gguf

The detection variant writes a single tensor graph + mean/std metadata. The recognition variant additionally writes the character vocabulary as a doctr.charset KV entry inside the GGUF file.

ABI

See include/doctr.h. The ABI is intentionally minimal:

doctr_det_ctx * doctr_det_init(const char * gguf_path);
int  doctr_det_run(doctr_det_ctx *, const float * rgb_chw, int h, int w,
                   float * out_prob, int * out_h, int * out_w);
void doctr_det_free(doctr_det_ctx *);

doctr_rec_ctx * doctr_rec_init(const char * gguf_path);
int  doctr_rec_run(doctr_rec_ctx *, const float * rgb_chw, int h, int w,
                   float * out_logits, int * out_T, int * out_C);
const char * doctr_rec_charset(doctr_rec_ctx *);
void doctr_rec_free(doctr_rec_ctx *);