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tesseract-ocr/tesseract ★ 76806 10819 APPLICATION

Tesseract is an open-source optical character recognition (OCR) engine originally developed by Hewlett-Packard and now maintained by Google and the community. It provides a C++ library (libtesseract) and a command-line tool (tesseract) that can recognize text from images in over 100 languages. The engine supports two recognition backends: a legacy engine based on character pattern matching with adaptive classifiers, and a modern LSTM-based neural network engine for line-level recognition. The architecture is modular, with separate components for page layout analysis (textord), character classification (classify), word recognition (wordrec), dictionary/DAG search (dict), and neural network layers (lstm). Training tools (src/training) allow users to build custom language models and fine-tune existing ones. The codebase is well-structured, extensively tested, and includes optimizations for SIMD (SSE, AVX, NEON, RVV). While primarily an application/library, it also has strong research elements in its LSTM training pipeline, feature extraction, and Unicode validation subsystems. The project has a long history but remains actively maintained, with recent commits addressing security, performance, and new architectures.

RATING: 9.3/10 MAINT: active DRIFT: low
#ocr #optical-character-recognition #computer-vision #c-plus-plus #lstm #neural-network #text-recognition #tesseract #tesseract-ocr #machine-learning #ocr-engine #hacktoberfest
▸ 4 reusable components found
PINTO0309/onnx2tf ★ 996 106 APPLICATION

onnx2tf is a production-ready ONNX to TensorFlow/TFLite conversion framework that employs a sophisticated pass-based optimization pipeline. The system converts ONNX models through preprocessing, lowering to an intermediate representation (ModelIR), applying 50+ optimization passes (layout transformation, operator fusion, quantization), and exporting to target formats including TFLite, SavedModel, PyTorch nn.Module, and TorchScript. Its architecture features clean separation between operator implementations, IR transformation passes, and backend-specific exporters, with the flatbuffer_direct backend providing 100x+ faster conversion by bypassing TensorFlow graph construction. The framework includes comprehensive accuracy validation tools, dynamic shape handling, and model surgery capabilities for intermediate layer extraction.

RATING: 8.8/10 MAINT: active DRIFT: low
#onnx-conversion #tflite-optimization #layout-optimization #operator-fusion #quantization-aware #cross-framework #pass-based-optimization #edge-deployment #deep-learning #machine-learning #model-converter #models #onnx #tensorflow #tensorflow-lite #tflite #docker #tfjs #android #quantization #keras #transformer #coreml #lstm #litert #pytorch #torchscript
▸ 5 reusable components found
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