feat(core): 扩展 API、完善日志与代码文档规范

- 公开颜色过滤与字符集限制扩展 API，修复宏路径
- 库内打印替换为 tracing 日志，清理遗留废弃代码
- 补充核心逻辑单元测试与 crate 元数据
- 开启 missing_docs 并统一 rustfmt/clippy 格式

ddddocr-core/examples/quick_start.rs

@@ -0,0 +1,63 @@
+//! 快速开始示例：演示 ddddocr-core 与引擎 crate 的解耦用法。
+//!
+//! 运行：`cargo run -p ddddocr-core --example quick_start`
+
+use ddddocr_core::error::{Result, TensorError};
+use ddddocr_core::traits::{InferenceEngine, Info, OcrEngine};
+use ddddocr_core::types::{ModelInfo, TensorInfo};
+use ddddocr_core::{ModelMetadata, Normalization, OcrBuilder, OcrOutput, Resize};
+
+/// 演示引擎：只实现接口，不接入真实 ONNX 运行时。
+struct DemoEngine {
+    meta: ModelMetadata,
+}
+
+impl Info for DemoEngine {
+    fn input_info(&self) -> Result<Vec<TensorInfo>> {
+        Ok(vec![])
+    }
+    fn output_info(&self) -> Result<Vec<TensorInfo>> {
+        Ok(vec![])
+    }
+    fn model_info(&self) -> Result<ModelInfo> {
+        Ok(ModelInfo {
+            inputs: vec![],
+            outputs: vec![],
+            providers: None,
+        })
+    }
+}
+
+impl InferenceEngine for DemoEngine {
+    type Output = OcrOutput;
+
+    fn inference(&self, input: ndarray::Array4<f32>) -> Result<Self::Output, TensorError> {
+        // 用全零 logits 模拟推理输出：[Steps, Classes]
+        let steps = input.shape()[2];
+        let classes = self.meta.charset.size();
+        Ok(OcrOutput::Logits(ndarray::Array2::zeros((steps, classes))))
+    }
+}
+
+impl OcrEngine for DemoEngine {
+    fn metadata(&self) -> &ModelMetadata {
+        &self.meta
+    }
+}
+
+fn main() {
+    let engine = DemoEngine {
+        meta: ModelMetadata::from_static_slice(
+            &["", "a", "b"],
+            false,
+            Resize::Fixed(64, 64),
+            1,
+            Normalization::ZeroToOne,
+        ),
+    };
+
+    let ocr = OcrBuilder::new().probability(true).build_with(&engine);
+    let image = image::DynamicImage::new_luma8(64, 64);
+    let result = ocr.predict(&image).expect("识别失败");
+    println!("识别结果: {result}");
+}