Multilingual Handwritten OCR using CLIP and Tesseract

Abstract

Optical Character Recognition (OCR) of handwritten text is an extremely challenging problem, particularly in multilingual and low-resource environments. Conventional OCR engines like Tesseract work well for printed text but not for handwriting because of extreme variations in style, language, and noise. The breakthroughs in multimodal models, especially CLIP (Contrastive Language–Image Pretraining), provide new avenues agnostic knowledge This paper discusses the possibility of combining CLIP with Tesseract to improve multilingual handwritten OCR, covering current methods, limitations, and future research directions.

Introduction

I. Introduction

Optical Character Recognition (OCR) is a crucial area of computer vision, widely used in:

• Document digitization

• Text extraction from images

• Handwriting recognition

Modern OCR systems for languages like English and French are highly developed, but for complex or ancient scripts (e.g., Malayalam), challenges remain—particularly with handwritten and curved text.

This study explores integrating traditional OCR tools (like Tesseract) with modern deep learning approaches (like CLIP) to enhance performance, especially in multilingual handwritten recognition.

II. Literature Survey

Traditional OCR:

• Earlier methods (e.g., template matching) lacked adaptability.

• Tesseract OCR, powered by LSTM, improved recognition but struggles with noise and cursive writing.

Deep Learning in OCR:

• CNNs and RNNs (e.g., CRNN) enable better feature extraction and sequence modeling.

• However, models often require retraining for new languages.

Multilingual OCR Challenges:

• Script variations, limited labeled data, and linguistic nuances.

• Datasets like IAM and RIMES exist, but multilingual coverage is still inadequate.

Vision-Language Models (CLIP):

• OpenAI’s CLIP aligns image and text embeddings, enabling zero-shot learning.

• Useful in script identification and contextual reranking of OCR outputs.

Hybrid Methods:

• Combine deep models (e.g., GANs, CLIP) for pre/post-processing with Tesseract for efficient OCR.

• Examples include CLIP-based language detection and reranking of Tesseract predictions.

Recent Advances:

• TrOCR (Microsoft) and MHTR use transformers for better multilingual recognition.

• Multimodal models like M4C show promise in text + vision tasks.

III. Methodology: Proposed Hybrid OCR Pipeline

1. Preprocessing & Segmentation

   • Grayscale conversion, adaptive thresholding, noise removal (morphological ops/CNN), and line segmentation.

2. Script Identification with CLIP

   • Use CLIP to compare image segments with language prompts and identify the script (e.g., Hindi, Arabic).

3. OCR Using Tesseract

   • Selects the relevant language model based on CLIP output.

   • Performs OCR using LSTM-based recognition and beam search for hypothesis generation.

4. Semantic Reranking with CLIP

   • Embeds each OCR output hypothesis using CLIP’s text encoder.

   • Selects the best match based on semantic similarity to the original image.

5. Post-processing

   • Includes spelling correction, grammar fixes, format restoration, and optional use of transformer-based language models like mBERT or XLM-R.

IV. Implementation Details

• Platform: Python

• Tools:

  • Tesseract OCR for recognition

  • NumPy for classification

  • OpenCV for image preprocessing

  • Tkinter for GUI

  • Google Translate API for language translation

• Process Flow:

  • Image captured or uploaded → Grayscaling & binarization → Noise removal → OCR via Tesseract → Language selection via Tkinter → Translation → Output in GUI

• Languages Supported: Hindi, Kannada, Marathi, Malayalam, Tamil, Telugu, Urdu

• Deployment: Implemented as an Android app for user accessibility and mobile compatibility.

Conclusion

Tesseract OCR is easily known as the strongest open-source OCR because of its accuracy and flexibility. It can be used for many OCR-related tasks because it can recognize multiple languages and image types. EasyOCR is another Python-based OCR tool and has a very simple work interface which supports text extraction and recognition through deep-learning-based methods. MMOCR is an advanced and mature OCR toolbox consisting of the latest features in text detection, recognition, and layout analyses. It is developed by OpenMMLab. It provides pre-trained models based on state-of-the-art deep-learning solutions, is agnostic to frameworks across many languages, and allows users to train and fine-tune models.I believe it would be great to compare five OCR libraries: Tesseract OCR, MMOCR, Paddle OCR, Easy OCR, and Keras OCR in a survey and analyze their performance using different languages like English, Hindi, Arabic, Tamil, and Malayalam. Obviously, out of all these libraries, Tesseract has a high percentage of recognition as it has focused on improving errors in Malayalam OCRs, reaching 93%, which is not bad at all. However, when tested against all of them, including those in English, Hindi, Tamil, and Arabic, Tesseract OCR is still on top. This mainly puts Tesseract OCR ahead with respect to highly competent Malayalam OCR tasks. Therefore, this will prove very helpful to the user for a better performance in Malayalam text recognition. Apart from this, a little survey might be useful comparing Tesseract with MMOCR, Paddle OCR, Easy OCR, and Keras OCR credentials for diverse languages like English, Hindi, Arabic, Tamil, and Malayalam. Besides, Tesseract does raise some standards in this bunch because it tries to lower the error occurrence for Malayalam OCRs, having quite a commendable 93% accuracy, which is very high. Notably, putting it to the test against the rest in English, Hindi, Tamil, and Arabic really proves promising: Tesseract OCR has been the best among all. Very much attributed to this, however, is Tesseract OCR on the grounds of quite a high performance in Malayalam OCR work. Thus, the user benefits significantly with Tesseract OCR for economical and accurate Malayalam text recognition.

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Copyright

Copyright © 2025 Abhishek Singh Sengar, Akash Kushwaha, Devendra , Ms. Aarti Attri, Dr. Sureshwati . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.