PDF Token Optimizer.
Extract text from PDF documents, clean up unnecessary whitespace, normalize bullets, remove page numbers, running headers, and filter out custom boilerplate content to minimize token count for ChatGPT, Claude, and LLMs.
Layout
Optimization Steps
How to use the PDF Token Optimizer
- Select and upload your document by dragging it into the dashed zone or clicking to browse your system.
- Configure the optimization settings in the Settings Bar by toggling Space Compression, Line Break Reduction, or Boilerplate Stripping.
- Enter custom headers or recurring words you want to remove in the "Custom Text to Strip" box (e.g., "Confidential, Draft, Report").
- Examine the side-by-side or stacked layout comparison view to review the original extracted raw text vs the optimized output.
- Compare real-time tiktoken (WASM cl100k_base) token counts to view your exact savings.
- Click the "Copy to Clipboard" button to copy the optimized payload directly into your ChatGPT, Claude, or other LLM prompt.
Frequently Asked Questions
No. ToolFesto prioritizes absolute security and privacy. All text extraction, sanitization, and token counting run entirely in your local browser using HTML5 File APIs, Web Workers, and WebAssembly. No files or text ever leave your device.
It lets you define custom keywords or phrases (e.g., "Project Report, Confidential"). The engine compiles these inputs into a dynamic regular expression and deletes any lines in the PDF that contain only these terms, ignoring case and leading/trailing spacing.
Yes! The extractor groups text blocks sharing the same Y-coordinate and detects visual horizontal gaps. Gaps exceeding a font-scaled threshold are joined with tab characters (\t) to retain tabular columns, while standard gaps are joined with spaces.
We use a WebAssembly port of @dqbd/tiktoken with the cl100k_base tokenizer. This is the exact encoding used by OpenAI's GPT-4, GPT-3.5-Turbo, and Claude models, ensuring 100% token count accuracy.
No. This tool requires digital, text-based PDF documents with embedded layout paths. Scanned documents containing only images of text do not have underlying text data and require OCR (Optical Character Recognition) to parse.
PDFs represent list bullets with various font glyphs (like solid circles, hollow circles, or letter artifacts). We normalize these to standard Markdown dashes ("- ") so that the LLM correctly parses them as structured lists.
About PDF Token Optimizer
The Ultimate PDF Text Extractor and LLM Context Token Optimizer
Large Language Models (LLMs) like OpenAI's GPT-4, Anthropic's Claude 3.5 Sonnet, and Google's Gemini have revolutionized how we analyze documents. However, feeding raw PDF text into these models is often highly inefficient and expensive. PDFs are notorious for introducing formatting anomalies, duplicate spaces, carriage returns, running headers, and repeating legal footers. Every single one of these formatting artifacts is encoded as tokens, consuming your precious context window limit and inflating your API costs.
Our PDF Token Optimizer acts as a pre-processing pipeline for your LLM prompts. By combining advanced client-side PDF text extraction with customized sanitization filters, it strips out token-wasting noise while preserving the meaningful structure of your document.
Preserving Table Structures with Visual Alignment
A common failure point of standard PDF-to-text converters is that they merge columns into a single jumbled line, completely destroying tabular data. To solve this, our tool analyzes the Y-coordinates of every text item on the page. Text items sharing the same vertical line are grouped together. Furthermore, we calculate the horizontal X-coordinate visual gap between adjacent words. If a gap exceeds an adaptive, font-scale-based threshold, it is automatically joined with a tab character (\t). This preserves column boundaries, allowing models like GPT-4 to read and process your tables perfectly.
How Space Compression and Line Break Reduction Work
Standard text extraction from PDFs often generates multiple consecutive spaces (especially in justified text blocks) and multiple blank lines. Under the hood, our engine implements standard sanitization chains:
- Space Compression: Compresses multiple consecutive spaces down to a single space, while safely ignoring single spaces and the tab characters used to separate table columns. It also cleans up justified text tab artifacts.
- Line Break Reduction: Cleans up whitespace-only lines and replaces three or more consecutive line breaks with exactly two breaks, preserving standard paragraph structures without wasted blank space.
- List Item Normalization: Standardizes common PDF bullet point artifacts (like
, circles, or standaloneomarkers) into standard Markdown list item dashes (-), making lists fully readable by LLMs.
Dynamic and User-Controlled Custom Text Stripping
Document reports and templates frequently feature repeating titles, drafts, or confidentiality markers on every page. Our tool includes a **Custom Text to Strip** input where you can enter a comma-separated list of terms (e.g. Project Report, Confidential, draft). The engine dynamically escapes these inputs and creates a regular expression to search and remove any lines consisting solely of these keywords, saving hundreds of tokens across long documents.
Related Tools
Text Compare Tool
Compare two texts side-by-side or unified to highlight differences in lines, words, or characters. Safe, fast, and completely client-side.
JSON Formatter
Format, validate, beautify, and minify JSON text client-side. Find exact line syntax errors instantly with detailed logs.
Text Formatter
Free online text formatter and case converter. Easily format text, change letter case (UPPERCASE, lowercase, Title Case, camelCase, snake_case), find & replace text, and clean up text layout instantly.