ollama run <模型名稱>ollama run llama2ollama listollama pull <模型名稱>ollama show <模型名稱>This guide will walk you through the process of combining multiple scanned images of book pages into a single PDF file on Mac OS X and using OCR (Optical Character Recognition) to convert the text into searchable, selectable electronic text.
First, we need to install a few tools to handle image and PDF processing, as well as perform OCR. The following tools are required:
pdfunite for merging PDFs.Install these tools using Homebrew:
brew install imagemagick # Install ImageMagick
brew install ocrmypdf # Install OCRMyPDF
brew install tesseract-lang # Install Tesseract language packs
brew install poppler # Install Poppler
If your scanned output is in image format (e.g., JPG), you can use ImageMagick to combine multiple image files into a single PDF document. This ensures that the subsequent OCR process works on a single PDF file.
To combine all .jpg images in the current directory into a single 001.pdf file:
magick *.jpg 001.pdfThis command uses the magick tool from ImageMagick to merge all .jpg files in the directory into one PDF file named 001.pdf.
Next, we use the ocrmypdf tool to perform OCR on the generated PDF file and save the output as a new file. In this step, we specify the OCR languages as English (eng) and Traditional Chinese (chi_tra).
Run the following command:
ocrmypdf -l eng+chi_tra+chi_tra_vert 001.pdf 001a.pdfThis command will apply OCR to the 001.pdf file and save the result as 001a.pdf. The -l eng+chi_tra+chi_tra_vert option indicates that both English and Traditional Chinese will be used for text recognition.
If you have multiple PDF files that need to be combined into a single final PDF, the pdfunite tool can do this easily. To merge the processed PDF files into a single output named combined_pdf.pdf, use the following command:
pdfunite 001a.pdf 002a.pdf 003a.pdf combined_pdf.pdfThis command merges 001a.pdf, 002a.pdf, and 003a.pdf into a single combined_pdf.pdf file.
By following these steps, you can:
ocrmypdf to perform OCR text recognition.This allows you to convert scanned images of book pages into a searchable, selectable, and copyable PDF document.
在這份說明文件中，我們將介紹如何在Mac OS X環境下，將多個掃描成圖片的書籍頁面合併為一個PDF檔案，並使用OCR（光學文字識別）將其中的文字轉換為可搜索和選取的電子文字。
首先，我們需要安裝一些工具來處理圖片和PDF，並進行OCR。以下是需要的工具：
pdfunite 等工具，用於合併PDF。使用 Homebrew 安裝這些工具：
brew install imagemagick # 安裝 ImageMagick
brew install ocrmypdf # 安裝 OCRMyPDF
brew install tesseract-lang # 安裝 Tesseract 語言包
brew install poppler # 安裝 Poppler
當掃描結果以圖片格式（如 JPG）儲存時，我們可以使用 ImageMagick 將多個圖片檔案合併為單一 PDF 文件。這樣做可以保證後續的 OCR 處理僅在一個 PDF 檔案上進行。
將當前目錄下的所有 .jpg 圖片檔合併為 001.pdf：
magick *.jpg 001.pdf這條指令將使用 ImageMagick 中的 magick 工具，將目錄中的所有 .jpg 檔案依序合併成一個 PDF 文件 001.pdf。
接下來，我們使用 ocrmypdf 工具來對生成的 PDF 檔案進行 OCR 處理，並將處理後的 PDF 儲存為新的文件。在此步驟中，我們指定使用英語（eng）和繁體中文（chi_tra）作為 OCR 的語言。
執行指令：
ocrmypdf -l eng+chi_tra+chi_tra_vert 001.pdf 001a.pdf這條指令會對 001.pdf 文件進行 OCR 文字識別，並將結果儲存為 001a.pdf。其中，-l eng+chi_tra+chi_tra_vert 表示同時使用英語和繁體中文進行文字識別。
如果你有多個 PDF 檔案需要合併為一個最終的 PDF 檔案，pdfunite 工具可以輕鬆做到這點。將處理過的 PDF 檔案合併為單一檔案 combined_pdf.pdf：
pdfunite 001a.pdf 002a.pdf 003a.pdf combined_pdf.pdf這條指令將 001a.pdf、002a.pdf 和 003a.pdf 三個檔案合併成一個 combined_pdf.pdf。
透過這些步驟，你可以：
ocrmypdf 進行 OCR 文字識別。這樣，你就能將掃描成圖片的書籍內容轉換為可以搜索、選取和複製文字的 PDF 電子檔。
This document outlines a comprehensive business model for an AI music generation service. As the demand for personalized and unique music continues to rise, leveraging artificial intelligence to create music can provide a competitive edge in the music industry. This model will explore various revenue streams, target markets, and operational strategies to ensure the service's success.
Content Creators: YouTubers, podcasters, and streamers looking for royalty-free music to enhance their content.
Advertising Agencies: Companies needing custom jingles or background music for advertisements.
Game Developers: Indie and large-scale game developers seeking unique soundtracks for their games.
Film and TV Production: Producers looking for original scores or soundtracks for their projects.
Musicians and Bands: Artists wanting to experiment with new sounds or collaborate with AI-generated music.
Subscription Model: Offer tiered subscription plans that provide users with access to different levels of music generation capabilities, including varying numbers of tracks per month, quality of music, and customization options.
Pay-Per-Track: Allow users to purchase individual tracks or licenses for specific uses, catering to those who may not want a subscription.
Custom Commissions: Provide a service where users can request custom music tailored to their specific needs, charging a premium for this personalized service.
Partnerships and Collaborations: Collaborate with platforms like YouTube, Twitch, and gaming companies to offer integrated music solutions, potentially sharing revenue from users who utilize the service.
Merchandising: Create and sell merchandise related to the AI-generated music, such as vinyl records, digital albums, or branded products.
Technology Development: Invest in developing a robust AI music generation algorithm that can create high-quality, diverse music across various genres.
User-Friendly Interface: Design an intuitive platform that allows users to easily generate and customize music tracks, ensuring a seamless user experience.
Marketing and Outreach: Utilize social media, influencer partnerships, and targeted advertising to reach potential users in the identified target markets.
Community Building: Foster a community of users who can share their experiences, provide feedback, and showcase how they use the generated music, enhancing user engagement and loyalty.
Legal Considerations: Ensure compliance with copyright laws and provide clear licensing agreements to protect both the service and its users.
The AI music generation service presents a unique opportunity to tap into the growing demand for personalized music solutions. By targeting diverse markets, implementing multiple revenue streams, and focusing on technology and user experience, this business model can establish a strong foothold in the music industry. With the right strategies in place, the service can not only thrive but also innovate the way music is created and consumed.
#
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
# 設定字體和字號
font = fm.FontProperties(fname='/Users/YOURNAME/Library/Fonts/jf-openhuninn-2.0.ttf', size=12)
tips = sns.load_dataset("tips")
sns.set()
sns.scatterplot(x="total_bill", y="tip", data=tips)
plt.title('餐廳小費關係圖', fontproperties=font)
plt.show()