To integrate with OpenClaw for Myanmar (Burmese) language recognition, you mainly need to:

• Install Tesseract with Myanmar language data
• Configure OpenClaw OCR pipeline
• Optimize preprocessing for Myanmar script
• Handle Unicode normalization and mixed-language text

Myanmar language support in Tesseract already exists through the mya language model and Myanmar.traineddata.

sudo apt update && sudo apt install -y \
 tesseract-ocr \
 libtesseract-dev \
 tesseract-ocr-mya

Verify installation: tesseract --list-langs → expected mya & eng. If missing, manually download:

sudo mkdir -p /usr/share/tesseract-ocr/5/tessdata
sudo wget https://github.com/tesseract-ocr/tessdata/raw/main/mya.traineddata \
 -O /usr/share/tesseract-ocr/5/tessdata/mya.traineddata
# Optional script model:
sudo wget https://github.com/tesseract-ocr/tessdata/raw/main/script/Myanmar.traineddata \
 -O /usr/share/tesseract-ocr/5/tessdata/Myanmar.traineddata

tesseract sample-myanmar.png stdout -l mya
# Mixed Burmese + English
tesseract sample.png stdout -l mya+eng

For OpenClaw integrations, mya+eng is usually better because Myanmar documents often contain English names, numbers, Latin abbreviations, and technical terms.

import pytesseract
from PIL import Image
img = Image.open("invoice_mm.png")
text = pytesseract.image_to_string(img, lang="mya+eng", config="--oem 1 --psm 6")
print(text)

Recommended configs: OEM=1 (LSTM), PSM=6 or 11, language mya+eng.

Typical architecture: Document Upload → Image Preprocessing → Tesseract OCR (mya+eng) → Unicode Cleanup → NER / Extraction → Structured JSON Output.

class MyanmarOCR:
 def extract(self, image_path):
 return pytesseract.image_to_string(
 image_path, lang="mya+eng", config="--oem 1 --psm 6"
 )

• Character stacking: multiple glyph layers
• Complex ligatures & combined characters
• Font inconsistency: Zawgyi vs Unicode
• Low-resource datasets & historical document quality

Research shows Myanmar OCR often benefits from hybrid pipelines using preprocessing + LSTM + post-correction.

pip install myanmar-tools rabbit

from myanmar_tools import ZawgyiDetector
detector = ZawgyiDetector()
score = detector.get_zawgyi_probability(text)
import rabbit
unicode_text = rabbit.zg2uni(text) # if Zawgyi detected

Normalization is crucial for accurate extraction with Tesseract.

import cv2
img = cv2.imread("scan.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
cv2.imwrite("clean.png", thresh)

Recommended: deskew, noise reduction, adaptive thresholding, DPI scaling to 300+, contrast enhancement.

FROM ubuntu:24.04
RUN apt update && apt install -y tesseract-ocr tesseract-ocr-mya python3-pip
RUN pip install pytesseract pillow opencv-python
WORKDIR /app
COPY . .
CMD ["python3", "app.py"]

from fastapi import FastAPI, UploadFile
import pytesseract
from PIL import Image
app = FastAPI()
@app.post("/ocr")
async def ocr(file: UploadFile):
 image = Image.open(file.file)
 text = pytesseract.image_to_string(image, lang="mya+eng")
 return {"text": text}

• OCR Engine: Tesseract 5
• Language: mya+eng
• Preprocessing: OpenCV
• Encoding normalization: Rabbit + Myanmar Tools
• Layout detection: YOLOv8 / Detectron2
• NLP extraction: spaCy / Transformers
• API: FastAPI, Queue: Celery/Redis

Use fine-tuned models: tesstrain.sh with custom Myanmar fonts, datasets like NRC cards, hospital records, government forms. Active research in Myanmar OCR fine-tuning.

• Printed document → --psm 6
• Sparse text → --psm 11
• Single line → --psm 7
• ID cards → --psm 4
• Forms → --psm 6

Myanmar NRC extraction, hospital records, pharmacy prescriptions, government forms, banking KYC, logistics manifests, education certificates, invoice digitization.

YOLO Layout Detection → OpenCV Cleanup → Tesseract OCR (mya+eng) → Unicode Normalization → LLM Correction Layer → Structured Extraction

Untuk mengintegrasikan OpenClaw dengan pengenalan bahasa Myanmar (Burma), Anda perlu:

• Menginstal Tesseract dengan data bahasa Myanmar
• Mengonfigurasi pipeline OCR OpenClaw
• Optimasi pra-pemrosesan untuk aksara Myanmar
• Menangani normalisasi Unicode dan teks campuran

Dukungan bahasa Myanmar di Tesseract sudah tersedia melalui model bahasa mya dan Myanmar.traineddata.

sudo apt update && sudo apt install -y \
 tesseract-ocr \
 libtesseract-dev \
 tesseract-ocr-mya

Verifikasi: tesseract --list-langs → diharapkan mya & eng. Jika tidak muncul, unduh manual:

sudo mkdir -p /usr/share/tesseract-ocr/5/tessdata
sudo wget https://github.com/tesseract-ocr/tessdata/raw/main/mya.traineddata \
 -O /usr/share/tesseract-ocr/5/tessdata/mya.traineddata
# Model script alternatif:
sudo wget https://github.com/tesseract-ocr/tessdata/raw/main/script/Myanmar.traineddata \
 -O /usr/share/tesseract-ocr/5/tessdata/Myanmar.traineddata

tesseract sample-myanmar.png stdout -l mya
# Campuran Burma + Inggris
tesseract sample.png stdout -l mya+eng

Untuk integrasi OpenClaw, mya+eng biasanya lebih baik karena dokumen Myanmar sering mengandung nama Inggris, angka, singkatan Latin, dan istilah teknis.

import pytesseract
from PIL import Image
img = Image.open("invoice_mm.png")
text = pytesseract.image_to_string(img, lang="mya+eng", config="--oem 1 --psm 6")
print(text)

Konfigurasi rekomendasi: OEM=1 (LSTM), PSM=6 atau 11, bahasa mya+eng.

Arsitektur umum: Upload Dokumen → Pra-pemrosesan Gambar → Tesseract OCR (mya+eng) → Pembersihan Unicode → NER / Ekstraksi → Output JSON terstruktur.

class MyanmarOCR:
 def extract(self, image_path):
 return pytesseract.image_to_string(
 image_path, lang="mya+eng", config="--oem 1 --psm 6"
 )

• Penumpukan karakter: lapisan glyph ganda
• Ligatur kompleks & karakter gabungan
• Ketidakkonsistenan font: Zawgyi vs Unicode
• Dataset sumber daya terbatas & kualitas dokumen historis

Penelitian menunjukkan OCR Myanmar mendapat manfaat dari pipeline hybrid menggunakan pra-pemrosesan + LSTM + koreksi-pasca.

pip install myanmar-tools rabbit

from myanmar_tools import ZawgyiDetector
detector = ZawgyiDetector()
score = detector.get_zawgyi_probability(text)
import rabbit
unicode_text = rabbit.zg2uni(text) # jika terdeteksi Zawgyi

Normalisasi sangat penting untuk ekstraksi akurat dengan Tesseract.

import cv2
img = cv2.imread("scan.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
cv2.imwrite("clean.png", thresh)

Rekomendasi: perbaikan kemiringan, reduksi noise, thresholding adaptif, skala DPI ke 300+, peningkatan kontras.

FROM ubuntu:24.04
RUN apt update && apt install -y tesseract-ocr tesseract-ocr-mya python3-pip
RUN pip install pytesseract pillow opencv-python
WORKDIR /app
COPY . .
CMD ["python3", "app.py"]

from fastapi import FastAPI, UploadFile
import pytesseract
from PIL import Image
app = FastAPI()
@app.post("/ocr")
async def ocr(file: UploadFile):
 image = Image.open(file.file)
 text = pytesseract.image_to_string(image, lang="mya+eng")
 return {"text": text}

• Mesin OCR: Tesseract 5
• Bahasa: mya+eng
• Pra-pemrosesan: OpenCV
• Normalisasi encoding: Rabbit + Myanmar Tools
• Deteksi tata letak: YOLOv8 / Detectron2
• Ekstraksi NLP: spaCy / Transformers
• API: FastAPI, Antrian: Celery/Redis

Gunakan model fine-tuning: tesstrain.sh dengan font Myanmar kustom, dataset seperti KTP NRC, rekam medis, formulir pemerintah. Penelitian aktif dalam fine-tuning OCR Myanmar.

• Dokumen tercetak → --psm 6
• Teks jarang → --psm 11
• Baris tunggal → --psm 7
• Kartu identitas → --psm 4
• Formulir → --psm 6

Ekstraksi NRC Myanmar, catatan rumah sakit, resep apotek, formulir pemerintah, KYC perbankan, manifes logistik, sertifikat pendidikan, digitalisasi faktur.

Deteksi Layout YOLO → Pembersihan OpenCV → OCR Tesseract (mya+eng) → Normalisasi Unicode → Lapisan Koreksi LLM → Ekstraksi Terstruktur