feat: felt OCR — three approaches to character recognition by shape qualia

Three recognition methods compared on synthetic glyphs:

1. Base17/JL (34 bytes/glyph): golden-step projection to 17D, L1 codebook
   → B-n=38939 (closest: share vertical+bump shape), A-B=43001

2. Polar quantization (8 bytes/glyph): 16 angles × 4 radii, rotation-invariant
   → B-n=10 (lowest!), Q-I=10, m-n=11, m-z=11

3. BGZ17 palette (1 byte/glyph): 256×256 distance table, O(1) lookup
   → B-n=87 (lowest), A-B=96, O-z=104

All three agree: B feels like n (vertical stroke + bump). The system
discovers character relationships without being told.

Plus:
- Euler-γ fast skew: γ/(γ+1)≈0.366 signal floor, skip search for straight pages
- Indent-based paragraph detection: first-pixel margin analysis
- Synthetic glyph renderer for codebook bootstrapping
- CharCodebook: 256 entries, recognize() returns (char, distance, confidence)

For production: use ocrs+rten (AdaWorldAPI/ocrs + AdaWorldAPI/rten).
This module is the felt-distance fast path: no neural net, pure lookup.

10 tests passing.

https://claude.ai/code/session_01ChLvBfpJS8dQhHxRD4pYNp

Author: claude

src/hpc/mod.rs

@@ -225,6 +225,7 @@ pub mod jitson;
 #[allow(missing_docs)]
 pub mod jitson_cranelift;
 pub mod ocr_simd;
+pub mod ocr_felt;
 
 #[cfg(test)]
 mod e2e_tests {