init
This commit is contained in:
Poison Flower
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#!/usr/bin/env python3
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"""
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Fully local, offline batch-OCR for scanned pages of a Japanese light novel
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(vertical text) via manga-ocr — no cloud account or credit card required.
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Usage:
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python local_mangaocr_ocr.py --input ./pages --output ./out
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Requirements:
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pip install manga-ocr opencv-python pillow natsort tqdm
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The first run downloads manga-ocr's model weights (~400 MB) from
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HuggingFace; after that everything runs offline. Without a GPU it runs on
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CPU, just slower (roughly 1-3 sec per column).
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How it works:
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1. Each page is cut into vertical text columns (by detecting whitespace
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gaps between columns — typical light novel layout).
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2. Columns are sorted right-to-left (the reading order for vertical
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Japanese text).
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3. Each column is OCR'd separately via manga-ocr.
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4. Results are joined back into per-page text.
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manga-ocr was trained mainly on manga speech bubbles (short text blocks),
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not dense full-page prose, so column segmentation matters a lot here for
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quality. If segmentation performs poorly on your scans (e.g. unusual
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layout), pass --whole-page to feed the model the full page without cutting
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it into columns (simpler, but usually lower quality on dense prose).
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"""
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import argparse
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import sys
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from pathlib import Path
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import cv2
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import numpy as np
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from natsort import natsorted
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from PIL import Image
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from tqdm import tqdm
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IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".webp", ".tif", ".tiff", ".bmp"}
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def find_columns(img_gray: np.ndarray, min_col_width: int = 12, gap_threshold: int = 4):
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"""Finds x-ranges of vertical text columns via a pixel-density projection.
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Returns a list of (x_start, x_end), sorted RIGHT-TO-LEFT (the reading
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order for vertical Japanese text).
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"""
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# Binarize: text (dark) -> white, background -> black
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_, binary = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
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# Dilate vertically a bit to merge characters within a column into one solid strip
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kernel = np.ones((25, 1), np.uint8)
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dilated = cv2.dilate(binary, kernel, iterations=1)
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col_sums = dilated.sum(axis=0) # text density per pixel column
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has_text = col_sums > 0
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columns = []
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x = 0
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width = len(has_text)
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while x < width:
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if has_text[x]:
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start = x
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while x < width and (has_text[x] or _gap_too_small(has_text, x, gap_threshold)):
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x += 1
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end = x
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if end - start >= min_col_width:
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columns.append((start, end))
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else:
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x += 1
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columns.sort(key=lambda c: c[0], reverse=True) # right-to-left
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return columns
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def _gap_too_small(has_text: np.ndarray, x: int, gap_threshold: int) -> bool:
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"""Checks whether a text-free gap is shorter than gap_threshold (to avoid splitting a column needlessly)."""
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if has_text[x]:
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return False
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end = x
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while end < len(has_text) and not has_text[end]:
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end += 1
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return (end - x) < gap_threshold
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def ocr_page(mocr, pil_img: Image.Image, whole_page: bool) -> str:
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if whole_page:
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return mocr(pil_img)
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img_np = np.array(pil_img.convert("L"))
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columns = find_columns(img_np)
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if not columns:
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# No columns detected (e.g. an illustration-only page) — fall back to the whole page
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return mocr(pil_img)
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texts = []
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for x_start, x_end in columns:
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pad = 4
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crop = pil_img.crop((max(0, x_start - pad), 0, min(pil_img.width, x_end + pad), pil_img.height))
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text = mocr(crop)
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if text.strip():
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texts.append(text.strip())
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return "\n".join(texts)
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def main():
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parser = argparse.ArgumentParser(description="Local batch-OCR for a light novel via manga-ocr")
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parser.add_argument("--input", required=True, help="Folder with scanned page images (jpg/png)")
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parser.add_argument("--output", required=True, help="Folder for the OCR results")
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parser.add_argument(
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"--whole-page", action="store_true",
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help="Skip column segmentation, feed the whole page to the model at once"
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)
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parser.add_argument("--start-page", type=int, default=1)
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args = parser.parse_args()
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input_dir = Path(args.input)
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output_dir = Path(args.output)
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output_dir.mkdir(parents=True, exist_ok=True)
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pages_dir = output_dir / "pages_txt"
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pages_dir.mkdir(exist_ok=True)
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images = [p for p in input_dir.iterdir() if p.suffix.lower() in IMAGE_EXTS]
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images = natsorted(images, key=lambda p: p.name)
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if not images:
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print(f"No images found in {input_dir}.", file=sys.stderr)
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sys.exit(1)
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print(f"Pages found: {len(images)}")
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print("Loading the manga-ocr model (downloads weights on first run, ~400 MB)...")
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from manga_ocr import MangaOcr
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mocr = MangaOcr()
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combined_path = output_dir / "combined.md"
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failed = []
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with open(combined_path, "w", encoding="utf-8") as combined_f:
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for idx, img_path in enumerate(tqdm(images, desc="OCR"), start=args.start_page):
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txt_out = pages_dir / f"{img_path.stem}.txt"
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if txt_out.exists():
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text = txt_out.read_text(encoding="utf-8")
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else:
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try:
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pil_img = Image.open(img_path)
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text = ocr_page(mocr, pil_img, args.whole_page)
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txt_out.write_text(text, encoding="utf-8")
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except Exception as e: # noqa: BLE001
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print(f"\nError on {img_path.name}: {e}", file=sys.stderr)
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failed.append(img_path.name)
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text = ""
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# Do NOT write a file to disk on failure — otherwise the next
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# run would see the file exists and skip retrying it.
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combined_f.write(f"\n\n<!-- page {idx}: {img_path.name} -->\n\n")
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combined_f.write(text)
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print(f"\nDone. Combined file: {combined_path}")
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print(f"Per-page files: {pages_dir}")
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if failed:
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print(f"\nFailed to OCR {len(failed)} page(s):")
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for name in failed:
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print(f" - {name}")
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print("Re-run the script with the same --output folder — already-done pages will not be redone.")
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if __name__ == "__main__":
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main()
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