| name: english_g2_jpqd | |
| description: English text recognition model (CRNN) optimized with JPQD quantization | |
| framework: ONNX | |
| task: text-recognition | |
| domain: computer-vision | |
| subdomain: optical-character-recognition | |
| model_info: | |
| architecture: CRNN (CNN + BiLSTM + CTC) | |
| language: English | |
| character_set: "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~" | |
| num_classes: 95 | |
| original_source: EasyOCR | |
| optimization: JPQD quantization | |
| specifications: | |
| input_shape: [1, 1, 32, 100] | |
| input_type: float32 | |
| input_format: Grayscale | |
| output_shape: [1, 25, 95] # sequence_length x num_classes | |
| output_type: float32 | |
| batch_size: dynamic | |
| sequence_length: 25 | |
| performance: | |
| original_size_mb: 14.4 | |
| optimized_size_mb: 8.5 | |
| compression_ratio: 3.97 | |
| inference_time_cpu_ms: ~10 | |
| accuracy_retention: ">95%" | |
| deployment: | |
| runtime: onnxruntime | |
| hardware: CPU-optimized | |
| precision: INT8 weights, FP32 activations | |
| memory_usage_mb: ~15 | |
| usage: | |
| preprocessing: | |
| - Convert to grayscale | |
| - Resize to 32x100 (height x width) | |
| - Normalize to [0,1] | |
| - Add batch and channel dimensions | |
| postprocessing: | |
| - Apply CTC decoding | |
| - Convert indices to characters | |
| - Remove blank tokens and duplicates | |
| supported_characters: | |
| digits: "0-9" | |
| lowercase: "a-z" | |
| uppercase: "A-Z" | |
| punctuation: "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~" | |
| training_data: | |
| type: Synthetic and real text images | |
| languages: English | |
| domains: Documents, natural scenes, printed text | |
| license: apache-2.0 | |
| tags: | |
| - text-recognition | |
| - english | |
| - crnn | |
| - lstm | |
| - ocr | |
| - onnx | |
| - quantized | |
| - jpqd |