refactor: 重构 OcrBuilder 过滤机制，实现零克隆、高性能的局部白名单

- 将有状态的有效索引缓存 `valid_indices` 从全局 `Charset` 剥离至局部 `OcrBuilder`
- 解码函数 `ctc_decode_to_string` 内部复用策略计算，通过 `has_any_match` 布尔开关实现全量免检短路加速
- 优化内存分配，根据 `CharsetRestrict` 策略动态精准计算 `HashSet` 初始容量，规避大词表空置浪费
- 增强鲁棒性，在策略与字库完全无交集时，自动触发智能降级，一键恢复全量识别并保持接口自洽

Cargo.toml

@@ -10,3 +10,5 @@ anyhow = "1.0.102"
 image = "0.25.10"
 base64 = "0.22.1"
 imageproc = { version = "0.26.2", default-features = true }
+serde = { version = "1.0.228", features = ["derive"] }
+serde_json = "1.0.150"

src/charset.rs

@@ -514,6 +514,150 @@ pub const CHARSET_BETA: &[&str] = &[
     "谬", "溝", "言", "哽", "婿", "猿", "跗", "獴", "俜", "呙", "弗", "凿", "窭", "铌", "友", "唉",
     "怫", "荘",
 ];
+
+pub const CHARSET_OLD: &[&str] = &["", "笤", "谴", "膀", "荔"];
+
 pub fn get_default_charset() -> Vec<String> {
     CHARSET_BETA.iter().map(|&s| s.to_string()).collect()
 }
+
+use std::borrow::Cow;
+use std::collections::{HashMap, HashSet};
+
+use std::ops::{Add, Deref};
+// 字符集范围类型
+/// 字符集范围限制组合子枚举
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub enum CharsetRestrict {
+    /// 纯整数 0-9
+    Digit,
+
+    /// 纯小写字母 a-z
+    Lowercase,
+
+    /// 纯大写字母 A-Z
+    Uppercase,
+
+    // /// 过滤模式：删除所有 ASCII 字母和数字（通常用于仅保留汉字、特殊标点）
+    // // ExcludeAlphanumeric,
+    // /// 自定义单字字符集，例如 "0123456789+-x/="
+    // Single(String),
+    /// 直接设置完整的 Token 白名单（支持多字 Token），例如 vec!["html".to_string()]
+    CustomList(Vec<String>),
+
+    /// 核心组合子：满足左边或右边任意一个条件即可（即 A + B 的并集逻辑）
+    /// 使用 Box 打破 Rust 编译期对递归枚举的无限大小限制
+    Or(Box<CharsetRestrict>, Box<CharsetRestrict>),
+}
+impl From<i32> for CharsetRestrict {
+    fn from(value: i32) -> Self {
+        match value {
+            0 => Self::Digit,
+            1 => Self::Lowercase,
+            2 => Self::Uppercase,
+            // 3 => Self::LowercaseUppercase,
+            // 4 => Self::LowercaseDigit,
+            // 5 => Self::UppercaseDigit,
+            // 6 => Self::LowercaseUppercaseDigit,
+            // 7 => Self::DefaultCharsetLowercaseUppercaseDigit,
+            _ => panic!("invalid charset range: {}", value),
+        }
+    }
+}
+impl CharsetRestrict {
+    /// 💡 辅助构造函数：直接在源头把用户的长字符串切碎，伪装成基础积木
+    pub fn from_chars(custom_str: &str) -> Self {
+        let tokens = custom_str.chars().map(|c| c.to_string()).collect();
+        CharsetRestrict::CustomList(tokens)
+    }
+    // 内部递归收集器：利用硬编码切片快速无损展开
+    pub(crate) fn matches(&self, s: &str) -> bool {
+        match self {
+            CharsetRestrict::Digit => s.len() == 1 && s.as_bytes()[0].is_ascii_digit(),
+            CharsetRestrict::Lowercase => s.len() == 1 && s.as_bytes()[0].is_ascii_lowercase(),
+            CharsetRestrict::Uppercase => s.len() == 1 && s.as_bytes()[0].is_ascii_uppercase(),
+            CharsetRestrict::CustomList(vec) => vec.iter().any(|t| t == s),
+            CharsetRestrict::Or(left, right) => left.matches(s) || right.matches(s),
+        }
+    }
+}
+// =====================================================================
+// 5. 优雅的魔法：重载 + 运算符 (实现 std::ops::Add)
+// =====================================================================
+
+/// 支持 `CharsetRestrict::Digit + CharsetRestrict::Lowercase`
+impl Add for CharsetRestrict {
+    type Output = Self;
+
+    fn add(self, rhs: Self) -> Self::Output {
+        CharsetRestrict::Or(Box::new(self), Box::new(rhs))
+    }
+}
+
+// ==========================================
+// 3. 字符集核心结构体 (重命名为 Charset)
+// ==========================================
+#[derive(Debug, Clone)]
+pub struct Charset {
+    // 使用 Cow 统一静态切片和动态读取的 Vec<String>，内部实现真正的零拷贝
+   pub tokens: Vec<Cow<'static, str>>,
+    // 反向查找表，保证字符转索引为 O(1)
+    pub char_to_idx: HashMap<Cow<'static, str>, usize>,
+    // 当前处于激活状态的有效索引缓存 (用于 CTC 解码前的过滤加速)
+    // pub valid_indices: HashSet<usize>,
+}
+
+impl Charset {
+    // 内部底层统一收拢构造
+    pub fn new(tokens: Vec<Cow<'static, str>>) -> Self {
+        let mut char_to_idx = HashMap::with_capacity(tokens.len());
+        for (idx, token) in tokens.iter().enumerate() {
+            char_to_idx.entry(token.clone()).or_insert(idx);
+            // 如果字符集有重复，保留第一个遇到的索引 (符合 Python .index 逻辑)
+            // char_to_idx.entry(token.to_string()).or_insert(idx);
+        }
+
+        Self {
+            tokens,
+            char_to_idx,
+        }
+    }
+
+    // --- 业务策略方法 ---
+
+    /// 将字符转为索引，不存在返回 -1 (保持与原 Python 库行为一致)
+    pub fn char_to_index(&self, char_str: &str) -> i32 {
+        if let Some(&idx) = self.char_to_idx.get(char_str) {
+            idx as i32
+        } else {
+            -1
+        }
+    }
+
+    /// 将索引转为字符引用，零拷贝。若越界返回 None
+    pub fn index_to_char_ref(&self, index: usize) -> Option<&str> {
+        self.tokens.get(index).map(|cow| cow.as_ref())
+    }
+    
+
+    pub fn is_valid_char(&self, char_str: &str) -> bool {
+        self.char_to_idx.get(char_str).is_some()
+    }
+    pub fn size(&self) -> usize {
+        self.tokens.len()
+    }
+    
+}
+
+// ==========================================
+// 4. 标准 Display 接口实现 (对应 __str__)
+// ==========================================
+impl std::fmt::Display for Charset {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(
+            f,
+            "Charset [Total Size: {}",
+            self.size(),
+        )
+    }
+}

src/lib.rs

@@ -2,6 +2,7 @@ mod charset;
 
 pub mod models;
 pub mod utils;
+mod model_metadata;
 
 use anyhow::Result;
 use image::DynamicImage;
@@ -9,9 +10,12 @@ use std::fmt::{Display, Formatter};
 
 // 关键点：直接使用 tract 重导出的 ndarray
 use crate::charset::get_default_charset;
+use crate::models::ocr::ColorRange;
 use models::det::Det;
 use models::loader::ModelSession;
 use models::ocr::Ocr;
+use crate::model_metadata::ModelMetadata;
+
 pub enum ModelSpec {
     /// 默认 OCR (使用内置路径)
     OcrModel,
@@ -19,12 +23,12 @@ pub enum ModelSpec {
     /// 自定义 OCR (路径由用户提供)
     CustomOcrModel {
         path: String,
-        charset: Vec<String>,
+        model_metadata: ModelMetadata,
     },
 }
 impl ModelSpec {
     // 将默认路径定义为内部关联常量
-    const DEFAULT_OCR_PATH: &'static str = "models/common.onnx";
+    const DEFAULT_OCR_PATH: &'static str = "models/common_sml2h3_f32.onnx";
     const DEFAULT_DET_PATH: &'static str = "models/common_det.onnx";
 }
 pub enum Runtime {
@@ -58,9 +62,9 @@ impl DdddOcrBuilder {
     }
 
     /// 设置自定义 OCR 路径
-    pub fn custom_ocr(mut self, path: String, charset: Vec<String>) -> Self {
+    pub fn custom_ocr(mut self, path: String, model_metadata: ModelMetadata) -> Self {
         // 直接重写枚举，替换掉之前的 Ocr 或 Det
-        self.mode = ModelSpec::CustomOcrModel { path, charset };
+        self.mode = ModelSpec::CustomOcrModel { path, model_metadata };
         self
     }
 
@@ -69,10 +73,10 @@ impl DdddOcrBuilder {
         let runtime = match self.mode {
             ModelSpec::OcrModel => Runtime::Ocr(Ocr::new(
                 ModelSpec::DEFAULT_OCR_PATH.into(),
-                get_default_charset(),
+                ModelMetadata::from_builtin_beta(),
             )?),
             ModelSpec::DetModel => Runtime::Det(Det::new(ModelSpec::DEFAULT_DET_PATH.into())?),
-            ModelSpec::CustomOcrModel { path, charset } => Runtime::Ocr(Ocr::new(path, charset)?),
+            ModelSpec::CustomOcrModel { path, model_metadata } => Runtime::Ocr(Ocr::new(path, model_metadata)?),
         };
 
         Ok(DdddOcr { runtime })
@@ -92,7 +96,7 @@ impl Display for DdddOcr {
 impl DdddOcr {
     pub fn classification(&self, img: &DynamicImage) -> Result<String> {
         match &self.runtime {
-            Runtime::Ocr(s) => s.predict(img, false),
+            Runtime::Ocr(s) => s.predict(img).run(),
             Runtime::Det(_) => Err(anyhow::anyhow!("当前模型是检测模型，无法执行 OCR")),
         }
     }
@@ -104,6 +108,40 @@ impl DdddOcr {
     }
 }
 
+struct Classification {}
+#[derive(Debug)]
+struct ClassificationBuilder {
+    img: DynamicImage,
+    png_fix: bool,
+    color_filter_colors: Option<Vec<ColorRange>>,
+    color_filter_custom_ranges: Option<Vec<ColorRange>>,
+}
+impl ClassificationBuilder {
+    pub fn new(img: DynamicImage) -> Self {
+        ClassificationBuilder {
+            img,
+            png_fix: false,
+            color_filter_colors: None,
+            color_filter_custom_ranges: None,
+        }
+    }
+    pub fn png_fix(mut self, value: bool) -> Self {
+        self.png_fix = value;
+        self
+    }
+    pub fn color_filter_colors(mut self, value: Vec<ColorRange>) -> Self {
+        self.color_filter_colors = Some(value);
+        self
+    }
+    pub fn color_filter_custom_ranges(mut self, value: Vec<ColorRange>) -> Self {
+        self.color_filter_custom_ranges = Some(value);
+        self
+    }
+    pub fn build(self) -> Classification {
+        Classification {}
+    }
+}
+
 #[cfg(test)]
 mod tests {
     #[test]

src/model_metadata.rs

@@ -0,0 +1,122 @@
+use crate::charset::{CHARSET_BETA, CHARSET_OLD, Charset, CharsetRestrict};
+use anyhow::{Result, anyhow};
+use serde::Deserialize;
+use std::borrow::Cow;
+use std::collections::{HashMap, HashSet};
+use std::fs::File;
+use std::io::Read;
+use std::path::Path;
+// =====================================================================
+// 1. 辅助定义的枚举与结构体
+// =====================================================================
+
+/// 图像缩放策略枚举
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum Resize {
+    /// 固定宽高，例如 (64, 64)
+    Fixed(u32, u32),
+    /// 高度固定，宽度根据原始比例动态计算（对应 Python 的 [-1, H]）
+    DynamicWidth(u32),
+    /// 单字识别的正方形切图（对应 Python 的 word 为 True 且 [-1, H]）
+    Square(u32),
+}
+
+/// 仅用于反序列化 JSON 的中间临时结构体（DTO）
+#[derive(Deserialize)]
+struct ModelMetadataDto {
+    charset: Vec<String>,
+    word: bool,
+    #[serde(alias = "image")]
+    resize: Vec<i32>,
+    channel: u8,
+}
+
+#[derive(Debug, Clone)]
+pub struct ModelMetadata {
+    /// 字符集管理器
+    pub charset:  Charset,
+    /// 是否为单字识别模型
+    pub word: bool,
+    /// 预处理的缩放策略
+    pub resize: Resize,
+    /// 图像通道数 (1 或 3)
+    pub channel: u8,
+}
+
+impl ModelMetadata {
+    // --- 优雅的工厂模式构造器 ---
+    /// 从预设的旧版字符集创建
+    pub fn from_builtin_old() -> Self {
+        Self::from_static_slice(CHARSET_OLD, false, Resize::Fixed(64, 64), 1)
+    }
+
+    /// 从预设的 Beta 版字符集创建
+    pub fn from_builtin_beta() -> Self {
+        Self::from_static_slice(CHARSET_BETA, false, Resize::Fixed(64, 64), 1)
+    }
+
+    /// 通用的静态切片转换构造器
+    pub fn from_static_slice(
+        slice: &[&'static str],
+        word: bool,
+        resize: Resize,
+        channel: u8,
+    ) -> Self {
+        let tokens: Vec<Cow<'static, str>> = slice.iter().map(|&s| Cow::Borrowed(s)).collect();
+        Self {
+            charset: Charset::new(tokens),
+            word,
+            resize,
+            channel,
+        }
+    }
+
+    /// 从外部外部 JSON 文件动态加载字符集
+    pub fn from_json_file<P: AsRef<Path>>(path: P) -> Result<Self> {
+        let path = path.as_ref();
+        if !path.exists() {
+            return Err(anyhow!("模型元数据配置文件不存在: {:?}", path));
+        }
+
+        let mut file = File::open(path)?;
+        let mut content = String::new();
+        file.read_to_string(&mut content)?;
+
+        let dto: ModelMetadataDto = serde_json::from_str(&content)
+            .map_err(|e| anyhow!("JSON 反序列化失败，请检查字段是否完整: {}", e))?;
+
+        // 1. 将 DTO 的字符串数组转化为强类型的 Charset
+        let tokens: Vec<Cow<'static, str>> =
+            dto.charset.into_iter().map(|s| Cow::Owned(s)).collect();
+        let charset = Charset::new(tokens);
+
+        // 2. 解析 resize 策略（重现 Python 的复杂条件判断）
+        if dto.resize.len() != 2 {
+            return Err(anyhow!(
+                "'resize (or image)' 字段必须是包含两个元素的数组，例如 [-1, 64]"
+            ));
+        }
+        let r0 = dto.resize[0];
+        let r1 = dto.resize[1];
+
+        let resize = if r0 == -1 {
+            if dto.word {
+                // 如果 word 为 true，且包含 -1，Python 里是 resize 为 (r1, r1) 的正方形
+                Resize::Square(r1 as u32)
+            } else {
+                // 如果 word 为 false，且包含 -1，Python 里是高度固定为 r1，宽度按原图比例缩放
+                Resize::DynamicWidth(r1 as u32)
+            }
+        } else {
+            // 正常的固定宽高
+            Resize::Fixed(r0 as u32, r1 as u32)
+        };
+
+        Ok(Self {
+            charset,
+            word: dto.word,
+            resize,
+            channel: dto.channel,
+        })
+    }
+}

src/models/ocr.rs

@@ -1,9 +1,12 @@
+use crate::charset::CharsetRestrict;
+use crate::model_metadata::ModelMetadata;
 use crate::models::base::ModelArgs;
+use crate::models::loader::{ModelLoader, ModelSession, ModelType};
 use crate::utils::image_io::png_rgba_white_preprocess;
 use crate::utils::image_processor::{convert_to_grayscale, resize_image};
-use crate::models::loader::{ModelLoader, ModelSession, ModelType};
 use anyhow::Context;
 use image::DynamicImage;
+use std::collections::HashSet;
 use tract_onnx::prelude::tract_ndarray::s;
 use tract_onnx::prelude::{
     DatumType, Graph, IntoTensor, RunnableModel, Tensor, TypedFact, TypedOp, tract_ndarray, tvec,
@@ -12,91 +15,9 @@ use tract_onnx::prelude::{
 // 颜色过滤的自定义范围：(低值RGB, 高值RGB)
 pub type ColorRange = ((u8, u8, u8), (u8, u8, u8));
 
-// 字符集范围类型
-#[derive(Debug, Clone)]
-pub enum CharsetRange {
-    All,                   // 所有字符
-    Digit,                 // 数字
-    Letter,                // 字母
-    Alphanumeric,          // 字母数字
-    Single(String),        // 单字符串
-    Multiple(Vec<String>), // 多个字符串
-    Range(char, char),     // 字符范围
-    Custom(Vec<char>),     // 自定义字符列表
-}
-#[derive(Debug, Clone)]
-pub struct PredictArgs {
-    /// 是否修复PNG格式问题
-    pub png_fix: bool,
-    /// 是否返回概率信息
-    pub probability: bool,
-    /// 颜色过滤：保留的颜色列表
-    pub color_filter_colors: Option<Vec<String>>,
-    /// 颜色过滤：自定义RGB范围
-    pub color_filter_custom_ranges: Option<Vec<ColorRange>>,
-    /// 字符集范围
-    pub charset_range: Option<CharsetRange>,
-}
-
-impl Default for PredictArgs {
-    fn default() -> Self {
-        Self {
-            png_fix: false,
-            probability: false,
-            color_filter_colors: None,
-            color_filter_custom_ranges: None,
-            charset_range: None,
-        }
-    }
-}
-
-impl PredictArgs {
-    pub fn new() -> Self {
-        Self::default()
-    }
-
-    // Builder 模式方法
-    pub fn png_fix(mut self, enabled: bool) -> Self {
-        self.png_fix = enabled;
-        self
-    }
-
-    pub fn probability(mut self, enabled: bool) -> Self {
-        self.probability = enabled;
-        self
-    }
-
-    pub fn color_filter_colors(mut self, colors: Vec<String>) -> Self {
-        self.color_filter_colors = Some(colors);
-        self
-    }
-
-    pub fn color_filter_custom_ranges(mut self, ranges: Vec<ColorRange>) -> Self {
-        self.color_filter_custom_ranges = Some(ranges);
-        self
-    }
-
-    pub fn charset_range(mut self, range: CharsetRange) -> Self {
-        self.charset_range = Some(range);
-        self
-    }
-
-    // 便捷构造方法
-    pub fn quick() -> Self {
-        Self::default()
-    }
-
-    pub fn with_probability() -> Self {
-        Self::default().probability(true)
-    }
-
-    pub fn with_png_fix() -> Self {
-        Self::default().png_fix(true)
-    }
-}
 pub struct Ocr {
-    session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
+    pub session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
-    charset: Vec<String>,
+    pub model_metadata: ModelMetadata,
 }
 impl ModelSession for Ocr {
     fn get_model_type(&self) -> ModelType {
@@ -107,21 +28,77 @@ impl ModelSession for Ocr {
     }
 }
 impl Ocr {
-    pub fn new(model_path: String, charset: Vec<String>) -> Result<Self, anyhow::Error> {
+    pub fn new(model_path: String, model_metadata: ModelMetadata) -> Result<Self, anyhow::Error> {
         let session = ModelLoader::load_model(&model_path)?.session;
-        Ok(Self { session, charset })
+        Ok(Self {
+            session,
+            model_metadata,
+        })
     }
-    pub fn predict(&self, image: &DynamicImage, png_fix: bool) -> Result<String, anyhow::Error> {
+    pub fn predict<'a>(&'a self, image: &'a DynamicImage) -> OcrBuilder<'a> {
-        let tensor = self.preprocess_image(image, png_fix)?;
+        OcrBuilder::new(self, image)
-        //
-        // let result = self.session.run(tvec!(tensor.into()))?;
-        // // 3. 解析结果
-        // // let output = result[0].to_array_view::<i64>()?;
-        let output = self.inference(tensor)?;
-        let output2 = self.process_text_output(&output)?;
-        Ok(self.ctc_decode_indices(&output2))
-        // Ok("ocr result".to_string())
     }
+}
+
+pub struct OcrBuilder<'a> {
+    ocr: &'a Ocr,
+    image: &'a DynamicImage,
+    /// 是否修复PNG格式问题
+    png_fix: bool,
+    /// 是否返回概率信息
+    #[allow(dead_code)]
+    probability: bool,
+    /// 颜色过滤：保留的颜色列表
+    color_filter_colors: Option<Vec<ColorRange>>,
+    /// 颜色过滤：自定义RGB范围
+    color_filter_custom_ranges: Option<Vec<ColorRange>>,
+    /// 字符集范围
+    charset_restrict: Option<CharsetRestrict>,
+}
+
+impl<'a> OcrBuilder<'a> {
+    // 初始化任务，设置默认参数
+    pub fn new(ocr: &'a Ocr, image: &'a DynamicImage) -> Self {
+        Self {
+            ocr,
+            image,
+            png_fix: false, // 默认值
+            probability: false,
+            color_filter_colors: None,
+            color_filter_custom_ranges: None,
+            charset_restrict: None,
+        }
+    }
+    pub fn png_fix(mut self, value: bool) -> Self {
+        self.png_fix = value;
+        self
+    }
+    pub fn color_filter_colors(mut self, value: Vec<ColorRange>) -> Self {
+        self.color_filter_colors = Some(value);
+        self
+    }
+    pub fn color_filter_custom_ranges(mut self, value: Vec<ColorRange>) -> Self {
+        self.color_filter_custom_ranges = Some(value);
+        self
+    }
+    pub fn charset_restrict(mut self, restrict: CharsetRestrict) -> Self {
+        self.charset_restrict = Some(restrict);
+        self
+    }
+
+    pub fn run(&self) -> anyhow::Result<String> {
+        let tensor = self.preprocess_image(self.image, self.png_fix)?;
+
+        let raw_tensor = self.inference(tensor)?;
+        let raw_indices = self.extract_indices_from_tensor(&raw_tensor)?;
+
+        // 步骤 2: 将索引切片 `&[i64]` 传给解码器进行 CTC 去重和字符映射
+        let final_text = self.ctc_decode_to_string(&raw_indices);
+
+        println!("最终识别出的验证码是: {}", final_text);
+        Ok(final_text)
+    }
+
     /// 对应 Python 的 _preprocess_image
     /// 负责：透明背景修复 -> 灰度化 -> 按比例 Resize -> 归一化 -> 4维张量转换
     fn preprocess_image(&self, img: &DynamicImage, png_fix: bool) -> anyhow::Result<Tensor> {
@@ -156,14 +133,16 @@ impl Ocr {
         // tract 的 run 会返回一个 Vec<TValue>，我们通常只需要第一个输出
         // let result = self.session.run(tvec!(tensor.into()))?;
         let mut result = self
+            .ocr
             .session
             .run(tvec!(tensor.into()))
             .context("执行模型推理失败")?;
         println!("模型输出原始数据: {:?}", result);
         Ok(result.remove(0).into_tensor())
     }
+
     /// 核心解析逻辑：将模型输出的各种维度/类型的 Tensor 转为字符索引序列
-    fn process_text_output(&self, raw_tensor: &Tensor) -> anyhow::Result<Vec<i64>> {
+    fn extract_indices_from_tensor(&self, raw_tensor: &Tensor) -> anyhow::Result<Vec<i64>> {
         let shape = raw_tensor.shape();
         println!("模型输出shape数据: {:?}", shape);
         let datum_type = raw_tensor.datum_type();
@@ -198,6 +177,9 @@ impl Ocr {
                         // 形状: [Steps, Classes] -> 已经剥离了 Batch 维度
                         (shape[0], shape[1], view.into_dyn())
                     }
+                    // 形状: [Classes] -> 单字符输出（对应 Python 的 ndim == 0 保护逻辑）
+                    // 我们把它虚构成一个 [1, Classes] 的 2D 矩阵来复用后面的 argmax 逻辑
+                    1 => (1, shape[0], view.into_dyn()),
                     _ => return Err(anyhow::anyhow!("不支持的输出维度: {:?}", shape)),
                 };
                 let array_2d = data_view.to_shape((steps, classes))?;
@@ -223,29 +205,107 @@ impl Ocr {
             )),
         }
     }
-    fn ctc_decode_indices(&self, predicted_indices: &[i64]) -> String {
+    /// 获取有效字符索引列表 (用于外部验证或过滤)
+    pub fn get_valid_indices(&self) -> HashSet<usize> {
+        let (_, valid_indices) = self.valid_indices();
+        valid_indices
+    }
+
+    fn valid_indices(&self) -> (bool, HashSet<usize>) {
+        let charset = &self.ocr.model_metadata.charset;
+        let tokens = &charset.tokens;
+        /// 根据传入的 CharsetRestrict 枚举策略，动态更新有效索引
+        // 1. 🧠 零克隆防御战：在局部判断并动态构建专属的白名单判定表
+        let mut valid_indices = HashSet::new();
+        let mut has_any_match = false;
+        if let Some(ref policy) = self.charset_restrict {
+            // 🧠 性能精算：根据限制策略的类型，智能分配合适的初始容量，1个字节都不浪费！
+            let estimated_capacity = match policy {
+                CharsetRestrict::Digit => 16,
+                CharsetRestrict::Lowercase | CharsetRestrict::Uppercase => 32,
+                CharsetRestrict::CustomList(vec) => vec.len() + 1, // 动态匹配列表大小
+                _ => 128, // 组合子（Or）等复杂情况，给个 128 黄金保底值
+            };
+            // 🚀 精准开辟内存，完美避开 8120 个槽位的巨大空置浪费
+            valid_indices = HashSet::with_capacity(estimated_capacity);
+
+            for (idx, token) in tokens.iter().enumerate() {
+                let token_str = token.as_ref();
+                // CTC Blank 空字符串无条件放行，其余交给超高性能的 matches
+                if token_str.is_empty() {
+                    valid_indices.insert(idx);
+                } else if policy.matches(token_str) {
+                    valid_indices.insert(idx);
+                    has_any_match = true;
+                }
+            }
+
+            // 终极防御：如果除了 Blank 之外，没有任何一个字符被匹配到
+            if !has_any_match {
+                valid_indices = (0..tokens.len()).collect();
+                println!("警告：当前限制策略与模型字符集完全没有交集！已自动恢复全量识别。");
+            }
+        }
+        (has_any_match, valid_indices)
+    }
+    /// 🌟 【按需延迟打印】：当用户真的需要“知道当前有哪些限制字符”时，一秒反查并打印
+    /// 这里的 &str 完美借用了自 tokens，依然是彻底的零拷贝！
+    pub fn get_valid_tokens(&self) -> Vec<&str> {
+        let charset = &self.ocr.model_metadata.charset;
+        let tokens = &charset.tokens;
+        self.get_valid_indices()
+            .iter()
+            .map(|&idx| tokens[idx].as_ref())
+            .collect()
+    }
+    pub fn valid_size(&self) -> usize {
+        self.get_valid_indices().len()
+    }
+    fn ctc_decode_to_string(&self, predicted_indices: &[i64]) -> String {
         println!("indices模型输出原始数据: {:?}", predicted_indices);
+        let charset = &self.ocr.model_metadata.charset;
+        let tokens = &charset.tokens;
+        // let valid_indices = &charset.valid_indices;
+
+        let (has_any_match, valid_indices) = self.valid_indices();
 
         // 对应 _ctc_decode_indices 的逻辑：去重、去 blank (0)
         let mut res = String::new();
         let mut prev_idx: i64 = -1;
 
         for &idx in predicted_indices {
-            // 1. 跳过连续重复的索引
+            // 1. CTC 去重：如果是连续重复的，直接跳过
-            // 2. 跳过 blank 字符 (假设索引 0 是 blank)
+            if idx == prev_idx {
-            if idx != prev_idx && idx != 0 {
+                continue;
-                if let Ok(u_idx) = usize::try_from(idx) {
+            }
-                    if let Some(char_str) = self.charset.get(u_idx) {
+            // 【关键核心】只要不是连续重复，立刻更新 prev_idx 状态，绝对不能被后续的过滤短路！
+            prev_idx = idx;
+
+            // 2. CTC 过滤 Blank (0)
+            if idx == 0 {
+                continue;
+            }
+            // 3. 类型安全转换
+            let u_idx = match usize::try_from(idx) {
+                Ok(u) => u,
+                Err(_) => continue,
+            };
+
+            // 4. 终极性能：既然你的有效索引库必然有全量或部分数据，这里直接进行 O(1) 包含校验
+            // 注意：去掉原本错误的 `!`
+            if has_any_match {
+                if !valid_indices.contains(&u_idx) {
+                    continue; // 不在有效字符集内，安全跳过
+                }
+            }
+
+            // 5. 字符映射
+            if let Some(char_str) = tokens.get(u_idx) {
                 res.push_str(char_str);
             } else {
-                        // 保护逻辑：如果模型预测的索引超出了字符集范围
                 eprintln!("警告: 预测索引 {} 超出字符集范围", u_idx);
             }
         }
-            }
-            prev_idx = idx;
-        }
-        println!("最终识别出的验证码是: {}", res);
         res
     }
 }

tests/ocr_test.rs

@@ -66,7 +66,7 @@ fn test_full_classification() {
     let ocr = DdddOcrBuilder::new().build().expect("模型加载失败");
 
     // 2. 加载测试图片
-    let img = image::open("samples/code2.png").expect("测试图片不存在");
+    let img = image::open("samples/code3.png").expect("测试图片不存在");
 
     // 3. 执行识别
     let result = ocr.classification(&img).expect("识别过程出错");