khmer_ocr_model_CRNN

Khmer OCR — ResNet + BiLSTM + CTC

This repository contains a deep learning model designed for Khmer and English Optical Character Recognition (OCR). It utilizes a ResNet backbone for spatial feature extraction, a bidirectional LSTM for sequence modeling, and Connectionist Temporal Classification (CTC) loss for alignment-free text recognition.

Model Details

Model Description

• Model type: khm_ocr_general_document
• Language(s): Khmer (km), English (en). Note: The model exhibits higher accuracy and optimization patterns for Khmer character compositions.
• Training State: Trained from scratch on a comprehensive dataset of printed text lines.
• Architecture:
  • CNN: ResNet blocks processing grayscale document line images downscaled to a fixed height.
  • RNN: 2-layer Bidirectional LSTM tracking character transitions.
  • Classifier: Linear layer mapping features to vocabulary indices decoded via greedy CTC.
• Charactor Error Rate: 0.005589
• Word Error Rate: 0.045868
• Training:
  • batch_size: 16
  • device: "cuda"
  • epochs: 30
  • learning_rate: 0.001
  • num_workers: 4
  • optimizer: "adam"
  • scheduler_factor: 0.5
  • scheduler_patience: 5

Out-of-Scope Use

While this model handles varied font distributions well, it is strictly an line-level text recognizer.

• Optimal on: Printed text documents featuring standard degradation, light blur, or low levels of scanner noise.
• Fails on: Heavily underlined text paths, highly blurred captures, handwritten manuscripts, or unwarped, rotated document images. Ensure input crops are straight and baseline-aligned before inference.

Recommendations

This architecture is under active development. While it successfully handles clean, isolated line crops, performance drops on extreme layouts. Preprocessing elements (like layout analysis and text-line deskewing) should be executed prior to feeding imagery to this network.

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How to Get Started with the Model

File Layout Requirements

Before running predictions, ensure your repository or local folder contains your model files and characters map structured as follows:

.
├── predict.py
└── khmer_ocr_model_CRNN/
    ├── best_model.pth
    └── char.json

predict.py

import argparse
import json
from pathlib import Path

import torch
import torch.nn as nn
from PIL import Image
import torchvision.transforms as T

HERE = Path(__file__).parent
IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif", ".webp"}


# ── Model Architecture ────────────────────────────────────────────────────────

class ResBlock(nn.Module):
    def __init__(self, in_ch, out_ch, stride=1):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, padding=1, bias=False),
            nn.BatchNorm2d(out_ch),
        )
        self.downsample = None
        if stride != 1 or in_ch != out_ch:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_ch, out_ch, 1, stride=stride, bias=False),
                nn.BatchNorm2d(out_ch),
            )
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        identity = x
        out = self.conv(x)
        if self.downsample is not None:
            identity = self.downsample(x)
        return self.relu(out + identity)


class ResNetBiLSTMCTC(nn.Module):
    def __init__(self, num_classes, hidden_size=256, num_layers=2, dropout=0.3):
        super().__init__()
        self.cnn = nn.Sequential(
            nn.Conv2d(1, 64, 3, padding=1, bias=False),       
            nn.BatchNorm2d(64),                                 
            nn.ReLU(inplace=True),                              
            nn.MaxPool2d(2, 2),                                 
            nn.Sequential(ResBlock(64, 64, stride=1)),          
            nn.Sequential(ResBlock(64, 128, stride=2)),         
            nn.Sequential(ResBlock(128, 256, stride=1)),        
        )
        self.rnn = nn.LSTM(
            input_size=256 * 16,
            hidden_size=hidden_size,
            num_layers=num_layers,
            bidirectional=True,
            dropout=dropout if num_layers > 1 else 0.0,
            batch_first=False,
        )
        self.classifier = nn.Linear(hidden_size * 2, num_classes)

    def forward(self, x):
        feat = self.cnn(x)                       # [B, 256, H', W']
        b, c, h, w = feat.shape
        seq = feat.permute(3, 0, 1, 2)          # [W', B, C, H']
        seq = seq.reshape(w, b, c * h)           # [W', B, C*H']
        out, _ = self.rnn(seq)                   # [W', B, 2*hidden]
        return self.classifier(out).log_softmax(2)  # [W', B, num_classes]


# ── Charset Parsing ───────────────────────────────────────────────────────────

def load_charset(path):
    """
    Builds an index-to-character mapping lookup from char.json files.
    Accommodates categories grouped by blocks, lists, or custom string token pairs.
    """
    with open(path, encoding="utf-8") as f:
        data = json.load(f)

    if isinstance(data, list):
        return {i: ch for i, ch in enumerate(data)}

    if isinstance(data, dict):
        known_categories = {"khmer", "latin", "digits", "special"}
        if known_categories & set(data.keys()):
            order = ["khmer", "latin", "digits", "special"]
            all_chars = "".join(data.get(cat, "") for cat in order)
            return {0: "<blank>", **{i + 1: ch for i, ch in enumerate(all_chars)}}

        try:
            return {int(k): v for k, v in data.items()}
        except ValueError:
            pass

        return {v: k for k, v in data.items()}

    raise ValueError(f"Unrecognized token map schema inside character file: {path}")


# ── Processing & Greedy Decoding ─────────────────────────────────────────────

def ctc_decode(log_probs, idx2char, blank=0):
    """Collapses consecutive duplicate indexes and strips blank tokens."""
    indices = log_probs.argmax(dim=1).tolist()
    chars, prev = [], None
    for idx in indices:
        if idx != prev and idx != blank:
            chars.append(idx2char.get(idx, "?"))
        prev = idx
    return "".join(chars)


_transform = T.Compose([
    T.ToTensor(),
    T.Normalize(mean=[0.5], std=[0.5]),
])

def preprocess(image_path, img_h=64, img_w=512):
    """Converts target image asset to grayscale, scales to expected shape, and adds batch dims."""
    img = Image.open(image_path).convert("L")
    img = img.resize((img_w, img_h), Image.BICUBIC)
    return _transform(img).unsqueeze(0)


def predict_one(model, image_path, idx2char, device, blank=0):
    tensor = preprocess(image_path).to(device)
    with torch.no_grad():
        log_probs = model(tensor)[:, 0, :]   # Extracted time steps: [Time, Classes]
    return ctc_decode(log_probs.cpu(), idx2char, blank=blank)


# ── Core Runtime Entrypoint ───────────────────────────────────────────────────

def main():
    parser = argparse.ArgumentParser(description="Khmer OCR Inference System Stack")
    parser.add_argument(
        "input",
        help="Target filepath to standalone line crop OR parent path containing image lists.",
    )
    parser.add_argument(
        "--model",
        default=str(HERE / "khmer_ocr_model_CRNN" / "best_model.pth"),
        help="Checkpoint parameter file destination location path.",
    )
    parser.add_argument(
        "--charset",
        default=str(HERE / "khmer_ocr_model_CRNN" / "char.json"),
        help="JSON configuration text format vocabulary parsing schema.",
    )
    parser.add_argument(
        "--output",
        default=str(HERE / "predictions.txt"),
        help="Target text document destination to record string output arrays.",
    )
    parser.add_argument(
        "--device",
        default="cuda" if torch.cuda.is_available() else "cpu",
        help="Hardware execution pipeline override runtime flag.",
    )
    args = parser.parse_args()

    device = torch.device(args.device)

    # Initialize vocabulary bounds
    idx2char = load_charset(args.charset)
    num_classes = max(idx2char.keys()) + 1

    # Instantiate weights mapping sequence layout
    model = ResNetBiLSTMCTC(num_classes=num_classes)
    state_dict = torch.load(args.model, map_location="cpu")
    model.load_state_dict(state_dict)
    model.to(device).eval()
    
    print(f"Model File   : {args.model}")
    print(f"Charset File : {args.charset} ({num_classes} distribution classes)")
    print(f"Target Device: {device}\n")

    input_path = Path(args.input)
    out_path = Path(args.output)

    # ── Path Evaluator Logic: Independent Image Evaluation ──────────────────
    if input_path.is_file():
        text = predict_one(model, input_path, idx2char, device)
        print(f"Image File : {input_path.name}")
        print(f"Prediction : {text}")
        with open(out_path, "w", encoding="utf-8") as f:
            f.write(f"{input_path.name}\t{text}\n")
        print(f"Saved logs : {out_path}")
        return

    # ── Path Evaluator Logic: Directory Iteration Loop ───────────────────────
    if input_path.is_dir():
        images = sorted(
            p for p in input_path.iterdir()
            if p.suffix.lower() in IMAGE_EXTS
        )
        if not images:
            print(f"Termination: No valid image file variations found inside directory context '{input_path}'")
            return

        print(f"Queued Processing Run: Found {len(images)} sequence elements inside directory structure.\n")
        with open(out_path, "w", encoding="utf-8") as f:
            for i, img_path in enumerate(images, 1):
                try:
                    text = predict_one(model, img_path, idx2char, device)
                except Exception as error_exception:
                    text = f"RUNTIME ERROR METRIC EXCEPTION: {error_exception}"
                print(f"[{i}/{len(images)}] {img_path.name} -> {text}")
                f.write(f"{img_path.name}\t{text}\n")

        print(f"\nExecution Pipeline Complete: Stream saved down cleanly into '{out_path}'")
        return

    print(f"Invalid Operation Error: Resource tracking index target reference '{input_path}' does not point to structural nodes.")


if __name__ == "__main__":
    main()