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use anyhow::anyhow;
use rten::Model;
use rten_imageproc::RotatedRect;
use rten_tensor::prelude::*;
use rten_tensor::NdTensor;
mod detection;
mod errors;
mod geom_util;
mod layout_analysis;
mod log;
mod preprocess;
mod recognition;
#[cfg(test)]
mod test_util;
mod text_items;
#[cfg(target_arch = "wasm32")]
mod wasm_api;
use detection::{TextDetector, TextDetectorParams};
use layout_analysis::find_text_lines;
use preprocess::prepare_image;
use recognition::{RecognitionOpt, TextRecognizer};
pub use preprocess::{DimOrder, ImagePixels, ImageSource, ImageSourceError};
pub use recognition::DecodeMethod;
pub use text_items::{TextChar, TextItem, TextLine, TextWord};
// nb. The "E" before "ABCDE" should be the EUR symbol.
const DEFAULT_ALPHABET: &str = " 0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~EABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
/// Configuration for an [OcrEngine] instance.
#[derive(Default)]
pub struct OcrEngineParams {
/// Model used to detect text words in the image.
pub detection_model: Option<Model>,
/// Model used to recognize lines of text in the image.
///
/// If using a custom model, you may need to adjust the
/// [`alphabet`](Self::alphabet) to match.
pub recognition_model: Option<Model>,
/// Enable debug logging.
pub debug: bool,
/// Method used to decode outputs of text recognition model.
pub decode_method: DecodeMethod,
/// Alphabet used for text recognition.
///
/// This is useful if you are using a custom recognition model with a
/// modified alphabet. If not specified a default alphabet will be used
/// which matches the one used to train the [original
/// models](https://github.com/robertknight/ocrs-models).
pub alphabet: Option<String>,
/// Set of characters that may be produced by text recognition.
///
/// This is useful when you need the text recognition model to
/// produce text that only includes a predefined set of characters, for
/// example only numbers or lower-case letters.
///
/// If this option is not set, text recognition may produce any character
/// from the recognition model's alphabet.
pub allowed_chars: Option<String>,
}
/// Detects and recognizes text in images.
///
/// OcrEngine uses machine learning models to detect text, analyze layout
/// and recognize text in an image.
pub struct OcrEngine {
detector: Option<TextDetector>,
recognizer: Option<TextRecognizer>,
debug: bool,
decode_method: DecodeMethod,
alphabet: String,
/// Indices of characters in `alphabet` that are excluded from recognition
/// output. See [`OcrEngineParams::allowed_chars`].
excluded_char_labels: Option<Vec<usize>>,
}
/// Input image for OCR analysis. Instances are created using
/// [OcrEngine::prepare_input]
pub struct OcrInput {
/// CHW tensor with normalized pixel values in [BLACK_VALUE, BLACK_VALUE + 1.].
pub(crate) image: NdTensor<f32, 3>,
}
impl OcrEngine {
/// Construct a new engine from a given configuration.
pub fn new(params: OcrEngineParams) -> anyhow::Result<OcrEngine> {
let detector = params
.detection_model
.map(|model| TextDetector::from_model(model, Default::default()))
.transpose()?;
let recognizer = params
.recognition_model
.map(TextRecognizer::from_model)
.transpose()?;
let alphabet = params
.alphabet
.unwrap_or_else(|| DEFAULT_ALPHABET.to_string());
let excluded_char_labels = params.allowed_chars.map(|allowed_characters| {
alphabet
.chars()
.enumerate()
.filter_map(|(index, char)| {
if !allowed_characters.contains(char) {
// Index `0` is reserved for the CTC blank character and
// `i + 1` is used as training label for character at
// index `i` of `alphabet` string.
//
// See https://github.com/robertknight/ocrs-models/blob/3d98fc655d6fd4acddc06e7f5d60a55b55748a48/ocrs_models/datasets/util.py#L113
Some(index + 1)
} else {
None
}
})
.collect::<Vec<_>>()
});
Ok(OcrEngine {
detector,
recognizer,
alphabet,
excluded_char_labels,
debug: params.debug,
decode_method: params.decode_method,
})
}
/// Preprocess an image for use with other methods of the engine.
pub fn prepare_input(&self, image: ImageSource) -> anyhow::Result<OcrInput> {
Ok(OcrInput {
image: prepare_image(image),
})
}
/// Detect text words in an image.
///
/// Returns an unordered list of the oriented bounding rectangles of each
/// word found.
pub fn detect_words(&self, input: &OcrInput) -> anyhow::Result<Vec<RotatedRect>> {
if let Some(detector) = self.detector.as_ref() {
detector.detect_words(input.image.view(), self.debug)
} else {
Err(anyhow!("Detection model not loaded"))
}
}
/// Detect text pixels in an image.
///
/// Returns an (H, W) tensor indicating the probability of each pixel in the
/// input being part of a text word. This is a low-level API that is useful
/// for debugging purposes. Use [detect_words](OcrEngine::detect_words) for
/// a higher-level API that returns oriented bounding boxes of words.
pub fn detect_text_pixels(&self, input: &OcrInput) -> anyhow::Result<NdTensor<f32, 2>> {
if let Some(detector) = self.detector.as_ref() {
detector.detect_text_pixels(input.image.view(), self.debug)
} else {
Err(anyhow!("Detection model not loaded"))
}
}
/// Perform layout analysis to group words into lines and sort them in
/// reading order.
///
/// `words` is an unordered list of text word rectangles found by
/// [OcrEngine::detect_words]. The result is a list of lines, in reading
/// order. Each line is a sequence of word bounding rectangles, in reading
/// order.
pub fn find_text_lines(
&self,
_input: &OcrInput,
words: &[RotatedRect],
) -> Vec<Vec<RotatedRect>> {
find_text_lines(words)
}
/// Recognize lines of text in an image.
///
/// `lines` is an ordered list of the text line boxes in an image,
/// produced by [OcrEngine::find_text_lines].
///
/// The output is a list of [TextLine]s corresponding to the input image
/// regions. Entries can be `None` if no text was found in a given line.
pub fn recognize_text(
&self,
input: &OcrInput,
lines: &[Vec<RotatedRect>],
) -> anyhow::Result<Vec<Option<TextLine>>> {
if let Some(recognizer) = self.recognizer.as_ref() {
recognizer.recognize_text_lines(
input.image.view(),
lines,
RecognitionOpt {
debug: self.debug,
decode_method: self.decode_method,
alphabet: &self.alphabet,
excluded_char_labels: self.excluded_char_labels.as_deref(),
},
)
} else {
Err(anyhow!("Recognition model not loaded"))
}
}
/// Prepare an image for input into the text line recognition model.
///
/// This method exists to help with debugging recognition issues by exposing
/// the preprocessing that [OcrEngine::recognize_text] does before it feeds
/// an image into the recognition model. Use [OcrEngine::recognize_text] to
/// recognize text.
///
/// `line` is a sequence of [RotatedRect]s that make up a line of text.
///
/// Returns a greyscale (H, W) image with values in [-0.5, 0.5].
pub fn prepare_recognition_input(
&self,
input: &OcrInput,
line: &[RotatedRect],
) -> anyhow::Result<NdTensor<f32, 2>> {
let Some(recognizer) = self.recognizer.as_ref() else {
return Err(anyhow!("Recognition model not loaded"));
};
let line_image = recognizer.prepare_input(input.image.view(), line);
Ok(line_image)
}
/// Return the confidence threshold applied to the output of the text
/// detection model to determine whether a pixel is text or not.
pub fn detection_threshold(&self) -> f32 {
self.detector
.as_ref()
.map(|detector| detector.threshold())
.unwrap_or(TextDetectorParams::default().text_threshold)
}
/// Convenience API that extracts all text from an image as a single string.
pub fn get_text(&self, input: &OcrInput) -> anyhow::Result<String> {
let word_rects = self.detect_words(input)?;
let line_rects = self.find_text_lines(input, &word_rects);
let text = self
.recognize_text(input, &line_rects)?
.into_iter()
.filter_map(|line| line.map(|l| l.to_string()))
.collect::<Vec<_>>()
.join("\n");
Ok(text)
}
}
#[cfg(test)]
mod tests {
use std::error::Error;
use rten::model_builder::{ModelBuilder, ModelFormat, OpType};
use rten::ops::{MaxPool, Transpose};
use rten::Dimension;
use rten::Model;
use rten_imageproc::{fill_rect, BoundingRect, Rect, RectF, RotatedRect};
use rten_tensor::prelude::*;
use rten_tensor::{NdTensor, NdTensorView, Tensor};
use super::{DimOrder, ImageSource, OcrEngine, OcrEngineParams, DEFAULT_ALPHABET};
/// Generate a dummy CHW input image for OCR processing.
///
/// The result is an RGB image which is black except for one line containing
/// `n_words` white-filled rects.
fn gen_test_image(n_words: usize) -> NdTensor<f32, 3> {
let mut image = NdTensor::zeros([3, 100, 200]);
for word_idx in 0..n_words {
for chan_idx in 0..3 {
fill_rect(
image.slice_mut([chan_idx]),
Rect::from_tlhw(30, (word_idx * 70) as i32, 20, 50),
1.,
);
}
}
image
}
/// Create a fake text detection model.
///
/// Takes a CHW input tensor with values in `[-0.5, 0.5]` and adds a +0.5
/// bias to produce an output "probability map".
fn fake_detection_model() -> Model {
let mut mb = ModelBuilder::new(ModelFormat::V1);
let mut gb = mb.graph_builder();
let input_id = gb.add_value(
"input",
Some(&[
Dimension::Symbolic("batch".to_string()),
Dimension::Fixed(1),
// The real model uses larger inputs (800x600). The fake uses
// smaller inputs to make tests run faster.
Dimension::Fixed(200),
Dimension::Fixed(100),
]),
None,
);
gb.add_input(input_id);
let output_id = gb.add_value("output", None, None);
gb.add_output(output_id);
let bias = Tensor::from_scalar(0.5);
let bias_id = gb.add_constant(bias.view());
gb.add_operator(
"add",
OpType::Add,
&[Some(input_id), Some(bias_id)],
&[output_id],
);
let graph = gb.finish();
mb.set_graph(graph);
let model_data = mb.finish();
Model::load(model_data).unwrap()
}
/// Create a fake text recognition model.
///
/// This takes an NCHW input with C=1, H=64 and returns an output with
/// shape `[W / 4, N, C]`. In the real model the last dimension is the
/// log-probability of each class label. In this fake we just re-interpret
/// each column of the input as a vector of probabilities.
///
/// Returns a `(model, alphabet)` tuple.
fn fake_recognition_model() -> (Model, String) {
let mut mb = ModelBuilder::new(ModelFormat::V1);
let mut gb = mb.graph_builder();
let output_columns = 64;
let input_id = gb.add_value(
"input",
Some(&[
Dimension::Symbolic("batch".to_string()),
Dimension::Fixed(1),
Dimension::Fixed(output_columns),
Dimension::Symbolic("seq".to_string()),
]),
None,
);
gb.add_input(input_id);
// MaxPool to scale width by 1/4: NCHW => NCHW/4
let pool_out = gb.add_value("max_pool_out", None, None);
gb.add_operator(
"max_pool",
OpType::MaxPool(MaxPool {
kernel_size: [1, 4].into(),
padding: [0, 0, 0, 0].into(),
strides: [1, 4].into(),
ceil_mode: false,
}),
&[Some(input_id)],
&[pool_out],
);
// Squeeze to remove the channel dim: NCHW/4 => NHW/4
let squeeze_axes = Tensor::from_vec(vec![1]);
let squeeze_axes_id = gb.add_constant(squeeze_axes.view());
let squeeze_out = gb.add_value("squeeze_out", None, None);
gb.add_operator(
"squeeze",
OpType::Squeeze,
&[Some(pool_out), Some(squeeze_axes_id)],
&[squeeze_out],
);
// Transpose: NHW/4 => W/4NH
let transpose_out = gb.add_value("transpose_out", None, None);
gb.add_operator(
"transpose",
OpType::Transpose(Transpose {
perm: Some(vec![2, 0, 1]),
}),
&[Some(squeeze_out)],
&[transpose_out],
);
gb.add_output(transpose_out);
let graph = gb.finish();
mb.set_graph(graph);
let model_data = mb.finish();
let model = Model::load(model_data).unwrap();
let alphabet = DEFAULT_ALPHABET.chars().take(output_columns - 1).collect();
(model, alphabet)
}
/// Return expected word locations for an image generated by
/// `gen_test_image(3)`.
///
/// The output boxes are slightly larger than in input image. This is
/// because the real detection model is trained to predict boxes that are
/// slightly smaller than the ground truth, in order to create a gap between
/// adjacent boxes. The connected components in model outputs are then
/// expanded in post-processing to recover the correct boxes.
fn expected_word_boxes() -> Vec<RectF> {
let [top, height] = [27, 25];
[
Rect::from_tlhw(top, -3, height, 56).to_f32(),
Rect::from_tlhw(top, 66, height, 57).to_f32(),
Rect::from_tlhw(top, 136, height, 57).to_f32(),
]
.into()
}
#[test]
fn test_ocr_engine_prepare_input() -> Result<(), Box<dyn Error>> {
let image = gen_test_image(3 /* n_words */);
let engine = OcrEngine::new(OcrEngineParams {
detection_model: None,
recognition_model: None,
..Default::default()
})?;
let input = engine.prepare_input(ImageSource::from_tensor(image.view(), DimOrder::Chw)?)?;
let [chans, height, width] = input.image.shape();
assert_eq!(chans, 1);
assert_eq!(width, image.size(2));
assert_eq!(height, image.size(1));
Ok(())
}
#[test]
fn test_ocr_engine_detect_words() -> Result<(), Box<dyn Error>> {
let n_words = 3;
let image = gen_test_image(n_words);
let engine = OcrEngine::new(OcrEngineParams {
detection_model: Some(fake_detection_model()),
recognition_model: None,
..Default::default()
})?;
let input = engine.prepare_input(ImageSource::from_tensor(image.view(), DimOrder::Chw)?)?;
let words = engine.detect_words(&input)?;
assert_eq!(words.len(), n_words);
let mut boxes: Vec<RectF> = words
.into_iter()
.map(|rotated_rect| rotated_rect.bounding_rect())
.collect();
boxes.sort_by_key(|b| [b.top() as i32, b.left() as i32]);
assert_eq!(boxes, expected_word_boxes());
Ok(())
}
// Test recognition using a dummy recognition model.
//
// The dummy model treats each column of the input image as a vector of
// character class probabilities. Pre-processing of the input will shift
// values from [0, 1] to [-0.5, 0.5]. CTC decoding of the output will ignore
// class 0 (as it represents a CTC blank) and repeated characters.
//
// Filling a single input row with "1"s will produce a single char output
// where the char's index in the alphabet is the row index - 1. ie. Filling
// the first row produces " ", the second row "0" and so on, using the
// default alphabet.
fn test_recognition(
params: OcrEngineParams,
image: NdTensorView<f32, 3>,
expected_text: &str,
) -> Result<(), Box<dyn Error>> {
let engine = OcrEngine::new(params)?;
let input = engine.prepare_input(ImageSource::from_tensor(image.view(), DimOrder::Chw)?)?;
// Create a dummy input line with a single word which fills the image.
let mut line_regions: Vec<Vec<RotatedRect>> = Vec::new();
line_regions.push(
[Rect::from_tlhw(0, 0, image.shape()[1] as i32, image.shape()[2] as i32).to_f32()]
.map(RotatedRect::from_rect)
.into(),
);
let lines = engine.recognize_text(&input, &line_regions)?;
assert_eq!(lines.len(), line_regions.len());
assert!(lines.get(0).is_some());
let line = lines[0].as_ref().unwrap();
assert_eq!(line.to_string(), expected_text);
Ok(())
}
#[test]
fn test_ocr_engine_recognize_lines() -> Result<(), Box<dyn Error>> {
let mut image = NdTensor::zeros([1, 64, 32]);
// Set the probability of character 1 in the alphabet ('0') to 1 and
// leave all other characters with a probability of zero.
image.slice_mut((.., 2, ..)).fill(1.);
let (rec_model, alphabet) = fake_recognition_model();
test_recognition(
OcrEngineParams {
detection_model: None,
recognition_model: Some(rec_model),
alphabet: Some(alphabet),
..Default::default()
},
image.view(),
"0",
)?;
Ok(())
}
#[test]
fn test_ocr_engine_filter_chars() -> Result<(), Box<dyn Error>> {
let mut image = NdTensor::zeros([1, 64, 32]);
// Set the probability of "0" to 0.7 and "1" to 0.3.
image.slice_mut((.., 2, ..)).fill(0.7);
image.slice_mut((.., 3, ..)).fill(0.3);
let (rec_model, alphabet) = fake_recognition_model();
test_recognition(
OcrEngineParams {
detection_model: None,
recognition_model: Some(rec_model),
alphabet: Some(alphabet),
..Default::default()
},
image.view(),
"0",
)?;
// Run recognition again but exclude "0" from the output.
let (rec_model, alphabet) = fake_recognition_model();
test_recognition(
OcrEngineParams {
detection_model: None,
recognition_model: Some(rec_model),
alphabet: Some(alphabet),
allowed_chars: Some("123456789".into()),
..Default::default()
},
image.view(),
"1",
)?;
Ok(())
}
}