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Chart comprehension presents significant challenges for machine learning models due to the diverse and intricate shapes of charts. Existing multimodal methods often over-look these visual features or fail to integrate them effectively for Chart Question Answering. To address this, we introduce CHARTFORMER, a unified framework that enhances chart component recognition by accurately identifying and classifying components such as bars, lines, pies, titles, legends, and axes. Additionally, we propose a novel Question-guided Deformable Co-Attention (QDCAt) mechanism, which fuses chart features encoded by Chart-former with the given question, leveraging the question's guidance to ground the correct answer. Extensive experiments demonstrate a 3.2% improvement in mAP over the baselines for chart component recognition. For ChartQA and OpenCQA tasks, our approach achieves improvements of 15.4% in accuracy and 0.8 in BLEU score, respectively, underscoring the robustness of our solution for detailed visual data interpretation across various applications.