add interchangeable ocr engines
This commit is contained in:
Brent Schroeter
parent
d5757e3811
commit
ac7e93a75b
11 changed files with 989 additions and 52 deletions
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@ -4,8 +4,8 @@ from time import time
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import numpy as np
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from archive_item import cache_item, fetch_item
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from engine import analyze_doc
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from microqa.items import cache_item, fetch_item
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from microqa.engine import analyze_doc
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def main():
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6
main.py
6
main.py
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@ -8,8 +8,8 @@ from time import sleep
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import requests
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from archive_item import fetch_item
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from engine import analyze_doc
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from microqa.items import fetch_item
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from microqa.engine import analyze_doc
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def main():
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@ -23,7 +23,7 @@ def main():
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"--cpus",
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type=int,
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help="number of concurrent subprocesses to use; higher is generally faster but consumes more resources",
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default=2,
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default=6,
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)
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parser.add_argument(
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"--earliest-review-date",
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0
microqa/__init__.py
Normal file
0
microqa/__init__.py
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@ -1,24 +1,21 @@
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import re
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from dataclasses import dataclass
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from dataclasses import dataclass, field
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from multiprocessing import Pool
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from sys import stdout
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import numpy as np
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import pytesseract
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from PIL import Image, ImageFilter
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from archive_item import ArchiveDoc
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from .items import ArchiveDoc
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from .ocr.tesseract import OcrEngine
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def analyze_doc(
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doc: ArchiveDoc, ocr_langs="eng+fra", parallel=1, use_cache=False, verbose=False
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):
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def analyze_doc(doc: ArchiveDoc, parallel=1, use_cache=False, verbose=False):
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if verbose:
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print(f"Loading {doc.name}...")
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stdout.flush()
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tasks: PageAnalysisTask = [
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PageAnalysisTask(im=leaf.image, ocr_langs=ocr_langs)
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PageAnalysisTask(im=leaf.image)
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for leaf in doc.fetch_leaves(use_cache=use_cache)
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]
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@ -45,7 +42,7 @@ class PageAnalysisTask:
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"""
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im: Image.Image
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ocr_langs: str = "eng+fra"
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ocr_langs: list[str] = field(default_factory=lambda: ["eng"])
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def analyze_page(task):
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@ -91,6 +88,8 @@ def analyze_page(task):
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)
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OCR_SCALE = 1
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# TODO: Refactor orientation detection logic into the OCR engine
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# modules.
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best_ocr_score = -1
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best_ocr_words = None
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best_ocr_orientation = -1
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@ -98,27 +97,23 @@ def analyze_page(task):
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im_rotated = task.im.resize(
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np.int_(np.array(task.im.size) * OCR_SCALE)
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).rotate(90 * orientation, expand=True)
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ocr = pytesseract.image_to_data(
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im_rotated,
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lang=task.ocr_langs,
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config=f"--oem 1 --dpi {int(300 * OCR_SCALE)} --tessdata-dir ./data/tessdata_fast-4.1.0",
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output_type=pytesseract.Output.DATAFRAME,
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).fillna({"text": ""})
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# Keep only words that Tesseract is confident in, and which are
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# oriented horizontally.
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words = ocr[(ocr["conf"] > 90) & (ocr["width"] > ocr["height"])]
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# Keep only alphabetical words of 4 or more characters.
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words = words[
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words.apply(
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lambda row: re.fullmatch(r"[a-zA-Z]{4,}", str(row["text"]))
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is not None,
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axis=1,
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)
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]
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if words.shape[0] > best_ocr_score:
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best_ocr_score = words.shape[0]
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ocr, ocr_meta = OcrEngine.process(im_rotated, languages=task.ocr_langs)
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if "page_angle" in ocr_meta:
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# OCR engine automatically accounts for page rotation.
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best_ocr_score = ocr.shape[0]
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# PaddleOCR counts rotation as degrees, in the opposite
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# direction as PIL's `Image.rotate()`
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best_ocr_orientation = (
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4 - round(((ocr_meta["page_angle"] + 360) % 360) / 90)
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) % 4
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best_ocr_words = ocr
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break
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if ocr.shape[0] > best_ocr_score:
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best_ocr_score = ocr.shape[0]
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best_ocr_orientation = orientation
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best_ocr_words = words
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best_ocr_words = ocr
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if best_ocr_score > 50:
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# Unlikely that another orientation will have more words, so
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# stop eating up CPU.
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@ -140,16 +135,10 @@ def analyze_page(task):
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np.int_(
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np.concat(
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(
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best_ocr_words["left"].to_numpy(),
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best_ocr_words["top"].to_numpy(),
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best_ocr_dims[0]
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- (
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best_ocr_words["left"] + best_ocr_words["width"]
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).to_numpy(),
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best_ocr_dims[1]
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- (
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best_ocr_words["top"] + best_ocr_words["height"]
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).to_numpy(),
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best_ocr_words["x0"].to_numpy(),
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best_ocr_words["y0"].to_numpy(),
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best_ocr_dims[0] - best_ocr_words["x1"].to_numpy(),
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best_ocr_dims[1] - best_ocr_words["y1"].to_numpy(),
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)
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)
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# Transform back into original image pixel density
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@ -129,6 +129,7 @@ class ArchiveDoc:
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# Indicate that leaf was not found in page_num list.
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page_index = None
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if not numbered_only or page_index is not None:
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with jp_zip.open(file_name) as jp_file:
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# Convert to single-channel greyscale ("L").
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image = Image.open(jp_file).convert("L")
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14
microqa/ocr/__init__.py
Normal file
14
microqa/ocr/__init__.py
Normal file
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@ -0,0 +1,14 @@
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"""
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This module contains interchangeable engines for optical character recognition,
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making it easy to swap implementations in and out based on speed and accuracy
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advantages without rewriting business logic.
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Each nested module exports a class named `OcrEngine` with a method named
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`process()`, which accepts a PIL `Image` and list of languages, and which
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returns a tuple containing a standardized `DataFrame` as well as a dictionary
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containing any additional specialized metadata made available from the
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underlying OCR engine. The `DataFrame` has columns
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`["text", "x0", "y0", "x1", "y1"]`, where X and Y coordinates are in pixels
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measured from the top left corner of the image. `x1` and `y1` values will be
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greater than or equal to the corresponding `x0` and `y0` values.
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"""
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99
microqa/ocr/paddleocr.py
Normal file
99
microqa/ocr/paddleocr.py
Normal file
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@ -0,0 +1,99 @@
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import numpy as np
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import pandas as pd
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from paddleocr import PaddleOCR
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from PIL import Image
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# Reuse OCR instances per language.
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instances: dict[str, PaddleOCR] = {}
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def convert_language(iso639_3_code: str) -> str:
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"""
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Format an ISO 639-3 language code for PaddleOCR.
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"""
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# TODO: Add remaining language code mappings (refer to:
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# https://github.com/PaddlePaddle/PaddleOCR/blob/eaede685bcaf22f287edf8865f4dd8d374acb75e/paddleocr/_pipelines/ocr.py#L306).
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mappings = {"eng": "en", "fra": "fr"}
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if iso639_3_code in mappings:
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return mappings[iso639_3_code]
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return iso639_3_code
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class OcrEngine:
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def process(
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image: Image, languages: list[str] = ["eng"]
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) -> tuple[pd.DataFrame, dict]:
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"""
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Use `paddleocr` to parse an image to a `DataFrame` with columns
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`["text", "x0", "y0", "x1", "y1"]`, where X and Y coordinates are in
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pixels measured from the top left corner of the image. `x1` and `y1`
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values will be greater than or equal to the corresponding `x0` and `y0`
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values.
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Compared to Tesseract, PaddleOCR is more accurate at low image
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resolutions and able to perform one-shot page angle detection. However,
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it is at least 10x slower per CPU core, as it is implemented with a deep
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learning model designed for GPU acceleration.
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PaddleOCR runs multi-core out of the box, so it is not necessary to
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process multiple pages in parallel to achieve good speed (though it
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still helps a little because the algorithm performs some single-threaded
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work at various points).
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Note: Specifying multiple languages will cause OCR to run once for each
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language and choose the result it thinks is best. Thus, it's
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recommended to stick to one language if at all possible.
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Troubleshooting: The PaddlePaddle core package has/had a bug triggering
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segfaults on ARM systems. Installing the nightly development build of
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`paddlepaddle` may be necessary to avoid it. Refer to:
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- [PaddleOCR issue 16609](https://github.com/PaddlePaddle/PaddleOCR/issues/16609)
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- [PaddlePaddle PR 75731](https://github.com/PaddlePaddle/Paddle/pull/75731)
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Params:
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image PIL image data.
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languages List of ISO-639-3 language codes fed to the OCR backend.
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"""
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best_result = None
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for language in languages:
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if language in instances:
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ocr_instance = instances[language]
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else:
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ocr_instance = PaddleOCR(
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use_doc_orientation_classify=True,
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use_doc_unwarping=False,
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use_textline_orientation=False,
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lang=convert_language(language),
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)
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instances[language] = ocr_instance
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[res] = ocr_instance.predict(np.array(image.convert("RGB")))
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if best_result is None or len(res["rec_texts"]) > len(
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best_result["rec_texts"]
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):
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best_result = res
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if best_result is None:
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raise Exception("no languages specified")
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res = best_result
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return (
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pd.DataFrame(
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{
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"text": res["rec_texts"],
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"x0": [x0 for [x0, _, _, _] in res["rec_boxes"]],
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"y0": [y0 for [_, y0, _, _] in res["rec_boxes"]],
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"x1": [x1 for [_, _, x1, _] in res["rec_boxes"]],
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"y1": [y1 for [_, _, _, y1] in res["rec_boxes"]],
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}
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),
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{
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"page_angle": res["doc_preprocessor_res"]["angle"],
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},
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)
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55
microqa/ocr/tesseract.py
Normal file
55
microqa/ocr/tesseract.py
Normal file
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@ -0,0 +1,55 @@
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import pandas as pd
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import pytesseract
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from PIL import Image
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class OcrEngine:
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def process(image: Image, languages: list[str]) -> tuple[pd.DataFrame, dict]:
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"""
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Use `pytesseract` to parse an image to a `DataFrame` with columns
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`["text", "x0", "y0", "x1", "y1"]`, where X and Y coordinates are in
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pixels measured from the top left corner of the image. `x1` and `y1`
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values will be greater than or equal to the corresponding `x0` and `y0`
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values.
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Note: Each Tesseract command runs single-threaded, so speed can be
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improved up to ~4x by distributing pages across processes running in
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parallel. In practice (at least on the M4 Pro chip) Tesseract seems to
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reach peak overall speed when running across approximately 6 processes
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in parallel. Increasing the CPU count further can cause performance to
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degrade sharply (regardless of RAM availability).
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Params:
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image PIL image data.
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languages List of ISO-639-3 language codes fed to the OCR backend.
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"""
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df = pytesseract.image_to_data(
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image,
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lang="+".join(languages),
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config="--oem 1 --tessdata-dir ./data/tessdata_fast-4.1.0",
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output_type=pytesseract.Output.DATAFRAME,
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)
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# Exclude words with relatively low confidence ratings.
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df = df[df["conf"] > 80]
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# Attempt to exclude words that seem vertically oriented.
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# TODO: Will this work for non-Latin scripts? Probably not all.
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df = df[(df["width"] / df["height"]) > 0.8]
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return (
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pd.DataFrame(
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{
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"text": df["text"],
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"x0": df["left"],
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"y0": df["top"],
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"x1": df["left"] + df["width"],
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"y1": df["top"] + df["height"],
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}
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),
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# We don't use any page-level metadata from the Tesseract output.
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{},
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)
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@ -6,3 +6,4 @@ watchexec = "latest"
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[settings]
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python.uv_venv_auto = true
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experimental = true
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@ -6,6 +6,8 @@ readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"numpy>=2.3.2",
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"paddleocr>=3.2.0",
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"paddlepaddle>=3.2.0",
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"pandas>=2.3.1",
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"pillow>=11.3.0",
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"pytesseract>=0.3.13",
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|
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780
uv.lock
generated
780
uv.lock
generated
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@ -2,8 +2,65 @@ version = 1
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revision = 3
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requires-python = ">=3.11"
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resolution-markers = [
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"python_full_version >= '3.12'",
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"python_full_version < '3.12'",
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"python_full_version >= '3.12' and sys_platform == 'darwin'",
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"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
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"python_full_version < '3.12' and sys_platform == 'darwin'",
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"python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
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"(python_full_version < '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
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]
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[[package]]
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name = "aistudio-sdk"
|
||||
version = "0.3.8"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "bce-python-sdk" },
|
||||
{ name = "click" },
|
||||
{ name = "prettytable" },
|
||||
{ name = "psutil" },
|
||||
{ name = "requests" },
|
||||
{ name = "tqdm" },
|
||||
]
|
||||
wheels = [
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
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]
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||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.11.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "idna" },
|
||||
{ name = "sniffio" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
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]
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||||
|
||||
[[package]]
|
||||
name = "bce-python-sdk"
|
||||
version = "0.9.46"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "future" },
|
||||
{ name = "pycryptodome" },
|
||||
{ name = "six" },
|
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|||
Loading…
Add table
Reference in a new issue