📷 Understanding Japanese Text from Images Using OCR and NLP

Processing Japanese text directly from images is a challenging task due to the unique structure of the language. Japanese sentences are written without spaces and combine three different writing systems Kanji, Hiragana, and Katakana often mixed with English and numbers. When this text appears inside images, the complexity increases further.

To solve this, we built a Japanese OCR-based Text Processing System that converts raw images into readable, meaningful, and visually annotated information.

🔍 Step 1: OCR-Based Text Extraction

The system begins by extracting Japanese text from images using Tesseract OCR with Japanese language support. Unlike plain text input, OCR also provides bounding box coordinates, allowing us to know where each word appears in the image. This positional information becomes crucial for later visual annotation.

🧠 Step 2: Linguistic Processing of Japanese Text

Once the text is extracted, it is processed using Japanese NLP tools. Since Japanese does not use spaces between words, tokenization is handled using libraries such as nagisa, which correctly identifies individual words and their parts of speech.

Kanji readings are generated using pykakasi, converting complex characters into hiragana, katakana, and romaji, making the text accessible even to beginners.

📚 Step 3: Dictionary-Based Meaning Resolution

Each extracted word is then searched in the JMdict dictionary using jamdict. This step provides accurate English meanings, alternative readings, and vocabulary-level explanations. Words not found in the dictionary such as proper nouns or technical terms are safely ignored or flagged.

🤖 Step 4: Contextual Understanding with LLMs

To go beyond word-level meaning, the system uses a Groq-powered LLM to analyze grammar patterns and generate a natural English translation. This helps explain particles, verb forms, and sentence structure in a way that traditional dictionaries cannot.

🎨 Step 5: Visual Annotation with Furigana

Finally, the system overlays the results directly onto the original image.

Furigana (hiragana readings) are rendered above Kanji

English meanings can be displayed below

Original layout is preserved using OCR bounding boxes

This transforms the image into an interactive learning resource, ideal for language learners and researchers.

🚀 Conclusion

By combining OCR, Japanese NLP, dictionary lookup, and LLM-based grammar analysis, this system bridges the gap between visual Japanese text and true understanding. Unlike traditional text summarization approaches, this pipeline focuses on reading assistance, interpretation, and visual clarity, making it especially powerful for real-world Japanese text found in images.