mem: reduce PaddleOCR rec_batch_num from 6 to 1

[KRRT7]

Reduce PaddleOCR rec_batch_num from 6 (default) to 1. Paddle's native inference engine allocates 500 MiB memory arena chunks proportional to recognition batch size. With batch_num=6, four chunks are allocated during text recognition. Setting it to 1 reduces this to one chunk.

Setting	Peak memory
rec_batch_num=6	7,184 MiB
rec_batch_num=1	2,684 MiB
Delta	-4,500 MiB (-62.6%)

Measured with memray run on layout-parser-paper-with-table.pdf through partition() with hi_res + PaddleOCR table OCR. On CPU, batch processing doesn't parallelize — it's sequential within predictor.run(). Smaller batches just allocate less workspace memory.

Reproduce

Requires unstructured[pdf], paddlepaddle, unstructured-paddleocr, and memray.

cat > /tmp/bench_paddle.py << 'SCRIPT'
from unstructured.partition.auto import partition
elements = partition(
    filename="example-docs/layout-parser-paper.pdf",
    strategy="hi_res",
    pdf_infer_table_structure=True,
    ocr_agent="unstructured.partition.utils.ocr_models.paddle_ocr.OCRAgentPaddle",
)
print(f"Partitioned: {len(elements)} elements")
SCRIPT

# Baseline (main branch, rec_batch_num=6):
git checkout main
memray run --native --trace-python-allocators -o /tmp/paddle_baseline.bin /tmp/bench_paddle.py
memray stats /tmp/paddle_baseline.bin | grep "Peak memory"

# With this change (rec_batch_num=1):
git checkout mem/paddle-rec-batch-num
memray run --native --trace-python-allocators -o /tmp/paddle_opt.bin /tmp/bench_paddle.py
memray stats /tmp/paddle_opt.bin | grep "Peak memory"