✨ Added #

• Stable Sum Detection: The scanner now identifies the most likely total amount by combining confidence and position tracking across multiple frames.
• Live Bounding Boxes: Detected receipt items are now outlined live on the camera preview, making it easier to see what's recognized in real time.

🛠️ Changed #

• Metadata Filtering: The engine is better at ignoring irrelevant lines like footer notes or fiscal metadata, keeping the product list clean.
• Enhanced Merge Logic: Optimized how multi-frame receipts are combined, improving structure and reliability even in fragmented scans.

🐛 Fixed #

• Incorrect Totals: Fixed an issue where prices from unrelated lines could falsely be interpreted as the receipt’s sum.
• Layout Glitches: Resolved occasional misalignments or line skips when processing longer or poorly lit receipts.

✨ Added #

• Smarter Scan Confidence Filtering: The recognition engine can now automatically ignore low-confidence items that might distort the total sum.
• Status Getter: Added a simple flag to detect if scanning is still in progress.
• Improved Example App: The example app now includes best practice instructions, live scan overlays, and a clearer UI for scanning and viewing receipts.

🛠️ Changed #

• Internal Optimizations: Minor refactoring and documentation improvements to ensure better readability and maintainability of the code.

🐛 Fixed #

• Sum Calculation Accuracy: Fixed cases where incorrect items could cause mismatches with the printed total.
• Scan Stability: Improved reliability of the scan process for varied receipt lengths and layouts.

✨ Added #

• Manual Receipt Acceptance: Added acceptReceipt() method to manually accept receipts with validation discrepancies
• Enhanced Validation Flow: Improved receipt validation with configurable thresholds and detailed status reporting
• Validation State Tracking: Added comprehensive validation states (complete, nearlyComplete, incomplete, invalid)
• Documentation: Added detailed documentation on validation workflow and manual acceptance

🛠️ Changed #

• Implementation Status Table: Improved formatting of status table in documentation
• Progress Reporting: Enhanced ScanProgress model with more detailed validation information
• Validation Logic: Refined match percentage calculation between calculated and declared sums

🐛 Fixed #

• Receipt Merging: Fixed edge cases in receipt merging that could lead to inaccurate total sums
• Stability Thresholds: Adjusted default stability thresholds for more reliable scanning

🐛 Fixed #

• Pass static analysis: Added curly braces to reach 50/50 points.

✨ Added #

• Intelligent product name normalization via ReceiptNormalizer.normalizeFromGroup
• Graph-based item ordering using PositionGraph with fuzzy matching and topological sort
• CachedReceipt consolidation with improved trust-based merging logic
• Parallelized parsing and graph resolution using Dart's compute() isolates
• Outlier filtering for OCR noise and trailing receipt lines
• Unit tests for name cleaning and group resolution

🛠️ Changed #

• Restructured core/ folder for better modularity
• Improved ReceiptParser._shrinkEntities() to robustly remove lines below sum/total
• Enhanced debug output when scanning receipts in ReceiptRecognizer

📚 Documentation #

• Added concise DartDoc to all public APIs, data models, and helpers

✅ Tests #

• Added group-based name cleaning tests
• Validated space vs. no-space consensus logic (e.g. "Co ke Zero" vs. "Coke Zero")

📚 Documentation #

• Added comprehensive dartdoc comments for all model classes in lib/src/receipt_models.dart

✨ Added #

• Optimizer Module: Introduced a new optimizer module to enhance the performance and efficiency of the receipt recognition pipeline.
• Product Name Detection Enhancements: Implemented advanced techniques for more accurate extraction and recognition of product names from receipts.

🛠️ Changed #

• Company Regex Enhancements: Updated regular expressions related to company name detection to enhance parsing accuracy.

🐛 Fixed #

• Product Name Extraction: Resolved issues where certain product names were not being accurately extracted due to inconsistent formatting.
• Company Name Detection: Fixed bugs in the regular expressions that led to incorrect company name identification in specific receipt formats.
• Parser Stability: Addressed edge cases in the parser that previously caused errors when processing receipts with uncommon layouts.

✅ Added #

• Unit Tests: Introduced unit tests to enhance code reliability and ensure consistent behavior across updates.

🛠️ Changed #

• Parser Refactoring:
  • Moved parsing methods to a dedicated parser module, improving code organization and maintainability.
  • Converted parser methods to static, facilitating easier access without instantiating classes.

🐛 Fixed #

• Company Name Recognition: Refined regular expressions related to company name detection, enhancing accuracy in identifying company names from receipts.

✨ Added #

• ReceiptWidget: A customizable Flutter widget that displays parsed receipt data in a layout resembling a real supermarket receipt.
• Advanced example demonstrating integration with a live video feed using the camera package.

🛠️ Changed #

• Updated README.md to include usage examples and details about models etc.

🐛 Fixed #

• Minor bug fixes and performance improvements.

✨ Added #

• Initial release of receipt_recognition.
• Core functionality to process images of receipts and extract structured data such as store name, items, and total amount using Google's ML Kit.
• Support for both Android and iOS platforms.