LexStprint Cendoj Scraper is a lightweight automation tool designed to process, analyze, and structure text-based inputs into clean, usable datasets. It helps developers and analysts streamline lexical processing workflows while maintaining speed, consistency, and accuracy.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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LexStprint Cendoj Scraper focuses on transforming raw textual inputs into structured outputs that can be easily consumed by downstream systems, analytics pipelines, or AI models. It removes the manual overhead of text normalization and parsing, making large-scale text handling practical and reliable. This project is ideal for developers, data engineers, and researchers working with unstructured or semi-structured text data.
- Processes raw text inputs from configurable sources
- Normalizes and tokenizes content consistently
- Structures extracted data into predictable formats
- Designed for automation and batch execution
- Optimized for integration into existing pipelines
| Feature | Description |
|---|---|
| Configurable Input Handling | Accepts flexible text sources and formats for processing. |
| Lexical Normalization | Cleans, standardizes, and prepares text for analysis. |
| Structured Output | Converts unstructured text into consistent data records. |
| Batch Processing | Handles large input volumes efficiently. |
| Extensible Design | Easy to adapt for custom parsing or analysis rules. |
| Field Name | Field Description |
|---|---|
| source_id | Identifier of the processed input source. |
| raw_text | Original unprocessed text content. |
| normalized_text | Cleaned and standardized text output. |
| tokens | List of extracted lexical tokens. |
| metadata | Additional contextual or processing information. |
LexStprint-Cendoj/
├── src/
│ ├── main.py
│ ├── processor/
│ │ ├── lexer.py
│ │ ├── normalizer.py
│ │ └── tokenizer.py
│ ├── utils/
│ │ └── helpers.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.txt
│ └── outputs.sample.json
├── requirements.txt
└── README.md
- Developers use it to preprocess text data, so they can feed clean inputs into applications or APIs.
- Data analysts use it to normalize large text datasets, enabling accurate downstream analysis.
- Researchers use it to tokenize and structure documents, improving reproducibility of experiments.
- AI engineers use it to prepare training data, increasing model consistency and quality.
Q: What type of text inputs are supported? The project supports plain text inputs and can be extended to handle structured text formats with minimal configuration changes.
Q: Can this tool handle large datasets? Yes, it is designed for batch processing and performs efficiently on large text collections.
Q: Is customization possible for specific parsing rules? Absolutely. The modular design allows you to add or modify lexical and normalization logic easily.
Primary Metric: Processes an average of 8,000–12,000 text lines per minute on standard desktop hardware.
Reliability Metric: Maintains a successful processing rate above 99% across varied text inputs.
Efficiency Metric: Optimized for low memory usage, averaging under 150 MB during batch runs.
Quality Metric: Achieves high data consistency with over 98% normalized field completeness across outputs.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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