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Oblevit at AR-MS NAKBA NLP 2026 Subtask 2: Hybrid CNN–BiLSTM–CTC Framework with Linguistic Refinement for Arabic Handwritten Manuscript Recognition
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Oblevit at AR-MS NAKBA NLP 2026 Subtask 2: Hybrid CNN–BiLSTM–CTC Framework with Linguistic Refinement for Arabic Handwritten Manuscript Recognition
Arabic handwritten manuscript recognition is challenging due to the cursive nature of the script, dot ambiguity, and document degradation. In this work, we propose an end-to-end OCR system based on a CNN–BiLSTM–CTC architecture. The model extracts visual features, captures sequential dependencies, and performs alignment-free training. Arabic-specific decoding and post-processing techniques are applied to reduce character and spacing errors. Experimental results show competitive performance in recognizing complex handwritten Arabic text.
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